Struct cv_convert::OpenCvMatAsTchTensor
source · [−]pub struct OpenCvMatAsTchTensor<'a> { /* private fields */ }
Expand description
Methods from Deref<Target = Tensor>
sourcepub fn as_ptr(&self) -> *const C_tensor
pub fn as_ptr(&self) -> *const C_tensor
Returns a pointer to the underlying C++ tensor.
The caller must ensures that the Rust tensor object outlives this pointer.
sourcepub fn as_mut_ptr(&mut self) -> *mut C_tensor
pub fn as_mut_ptr(&mut self) -> *mut C_tensor
Returns a mutable pointer to the underlying C++ tensor.
The caller must ensures that the Rust tensor object outlives this pointer.
sourcepub fn size1(&self) -> Result<i64, TchError>
pub fn size1(&self) -> Result<i64, TchError>
Returns the tensor size for single dimension tensors.
sourcepub fn size2(&self) -> Result<(i64, i64), TchError>
pub fn size2(&self) -> Result<(i64, i64), TchError>
Returns the tensor sizes for two dimension tensors.
sourcepub fn size3(&self) -> Result<(i64, i64, i64), TchError>
pub fn size3(&self) -> Result<(i64, i64, i64), TchError>
Returns the tensor sizes for three dimension tensors.
sourcepub fn size4(&self) -> Result<(i64, i64, i64, i64), TchError>
pub fn size4(&self) -> Result<(i64, i64, i64, i64), TchError>
Returns the tensor sizes for four dimension tensors.
sourcepub fn size5(&self) -> Result<(i64, i64, i64, i64, i64), TchError>
pub fn size5(&self) -> Result<(i64, i64, i64, i64, i64), TchError>
Returns the tensor sizes for five dimension tensors.
sourcepub fn size6(&self) -> Result<(i64, i64, i64, i64, i64, i64), TchError>
pub fn size6(&self) -> Result<(i64, i64, i64, i64, i64, i64), TchError>
Returns the tensor sizes for six dimension tensors.
sourcepub fn stride1(&self) -> Result<i64, TchError>
pub fn stride1(&self) -> Result<i64, TchError>
Returns the tensor strides for single dimension tensors.
sourcepub fn stride2(&self) -> Result<(i64, i64), TchError>
pub fn stride2(&self) -> Result<(i64, i64), TchError>
Returns the tensor strides for two dimension tensors.
sourcepub fn stride3(&self) -> Result<(i64, i64, i64), TchError>
pub fn stride3(&self) -> Result<(i64, i64, i64), TchError>
Returns the tensor strides for three dimension tensors.
sourcepub fn stride4(&self) -> Result<(i64, i64, i64, i64), TchError>
pub fn stride4(&self) -> Result<(i64, i64, i64, i64), TchError>
Returns the tensor strides for four dimension tensors.
sourcepub fn stride5(&self) -> Result<(i64, i64, i64, i64, i64), TchError>
pub fn stride5(&self) -> Result<(i64, i64, i64, i64, i64), TchError>
Returns the tensor strides for five dimension tensors.
sourcepub fn stride6(&self) -> Result<(i64, i64, i64, i64, i64, i64), TchError>
pub fn stride6(&self) -> Result<(i64, i64, i64, i64, i64, i64), TchError>
Returns the tensor strides for six dimension tensors.
sourcepub fn f_kind(&self) -> Result<Kind, TchError>
pub fn f_kind(&self) -> Result<Kind, TchError>
Returns the kind of elements stored in the input tensor. Returns an error on undefined tensors and unsupported data types.
sourcepub fn kind(&self) -> Kind
pub fn kind(&self) -> Kind
Returns the kind of elements stored in the input tensor. Panics an error on undefined tensors and unsupported data types.
sourcepub fn print(&self)
pub fn print(&self)
Prints the input tensor.
Caution: this uses the C++ printer which prints the whole tensor even if it is very large.
sourcepub fn f_double_value(&self, idx: &[i64]) -> Result<f64, TchError>
pub fn f_double_value(&self, idx: &[i64]) -> Result<f64, TchError>
Returns a double value on tensors holding a single element. An error is returned otherwise.
sourcepub fn f_int64_value(&self, idx: &[i64]) -> Result<i64, TchError>
pub fn f_int64_value(&self, idx: &[i64]) -> Result<i64, TchError>
Returns an int value on tensors holding a single element. An error is returned otherwise.
sourcepub fn double_value(&self, idx: &[i64]) -> f64
pub fn double_value(&self, idx: &[i64]) -> f64
Returns a double value on tensors holding a single element. Panics otherwise.
sourcepub fn int64_value(&self, idx: &[i64]) -> i64
pub fn int64_value(&self, idx: &[i64]) -> i64
Returns an int value on tensors holding a single element. Panics otherwise.
sourcepub fn requires_grad(&self) -> bool
pub fn requires_grad(&self) -> bool
Returns true if gradient are currently tracked for this tensor.
sourcepub fn is_mkldnn(&self) -> bool
pub fn is_mkldnn(&self) -> bool
Returns true if the tensor is compatible with MKL-DNN (oneDNN).
sourcepub fn f_backward(&self) -> Result<(), TchError>
pub fn f_backward(&self) -> Result<(), TchError>
Runs the backward pass, populating the gradient tensors for tensors which gradients are tracked.
Gradients tracking can be turned on via set_requires_grad
.
sourcepub fn backward(&self)
pub fn backward(&self)
Runs the backward pass, populating the gradient tensors for tensors which gradients are tracked.
Gradients tracking can be turned on via set_requires_grad
.
Panics if the C++ api returns an exception.
sourcepub fn f_copy_data_u8(
&self,
dst: &mut [u8],
numel: usize
) -> Result<(), TchError>
pub fn f_copy_data_u8(
&self,
dst: &mut [u8],
numel: usize
) -> Result<(), TchError>
Copies numel
elements from self
to dst
.
sourcepub fn f_internal_amp_non_finite_check_and_unscale(
&mut self,
found_inf: &mut Tensor,
inv_scale: &Tensor
) -> Result<(), TchError>
pub fn f_internal_amp_non_finite_check_and_unscale(
&mut self,
found_inf: &mut Tensor,
inv_scale: &Tensor
) -> Result<(), TchError>
Unscale tensor while checking for infinities.
found_inf
is a singleton tensor that is used to record the
presence of infinite values. inv_scale
is a scalar containing
the inverse scaling factor. This method is only available
for CUDA tensors.
sourcepub fn internal_amp_non_finite_check_and_unscale(
&mut self,
found_inf: &mut Tensor,
inv_scale: &Tensor
)
pub fn internal_amp_non_finite_check_and_unscale(
&mut self,
found_inf: &mut Tensor,
inv_scale: &Tensor
)
Unscale tensor while checking for infinities.
found_inf
is a singleton tensor that is used to record the
presence of infinite values. inv_scale
is a scalar containing
the inverse scaling factor. This method is only available
for CUDA tensors.
sourcepub fn copy_data_u8(&self, dst: &mut [u8], numel: usize)
pub fn copy_data_u8(&self, dst: &mut [u8], numel: usize)
Copies numel
elements from self
to dst
.
sourcepub fn f_copy_data<T>(
&self,
dst: &mut [T],
numel: usize
) -> Result<(), TchError> where
T: Element,
pub fn f_copy_data<T>(
&self,
dst: &mut [T],
numel: usize
) -> Result<(), TchError> where
T: Element,
Copies numel
elements from self
to dst
.
sourcepub fn copy_data<T>(&self, dst: &mut [T], numel: usize) where
T: Element,
pub fn copy_data<T>(&self, dst: &mut [T], numel: usize) where
T: Element,
Copies numel
elements from self
to dst
.
sourcepub fn shallow_clone(&self) -> Tensor
pub fn shallow_clone(&self) -> Tensor
Returns a new tensor that share storage with the input tensor.
sourcepub fn f_get(&self, index: i64) -> Result<Tensor, TchError>
pub fn f_get(&self, index: i64) -> Result<Tensor, TchError>
Gets the sub-tensor at the given index.
sourcepub fn f_copy_(&mut self, src: &Tensor) -> Result<(), TchError>
pub fn f_copy_(&mut self, src: &Tensor) -> Result<(), TchError>
Copies values from the argument tensor to the input tensor.
sourcepub fn copy_(&mut self, src: &Tensor)
pub fn copy_(&mut self, src: &Tensor)
Copies values from the argument tensor to the input tensor.
sourcepub fn save<T>(&self, path: T) -> Result<(), TchError> where
T: AsRef<Path>,
pub fn save<T>(&self, path: T) -> Result<(), TchError> where
T: AsRef<Path>,
Saves a tensor to a file.
The file format is the same as the one used by the PyTorch C++ API.
pub fn f_internal_and_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_and_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_iand_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_iand_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_ilshift_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_ilshift_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_ior_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_ior_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_irshift_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_irshift_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_ixor_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_ixor_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_lshift_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_lshift_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_or_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_or_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_rshift_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_rshift_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_xor_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_xor_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_adaptive_avg_pool2d(
&self,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_internal_adaptive_avg_pool2d_backward(
&self,
grad_output: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_adaptive_avg_pool3d(
&self,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_internal_adaptive_avg_pool3d_backward(
&self,
grad_output: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_add_batch_dim(
&self,
batch_dim: i64,
level: i64
) -> Result<Tensor, TchError>
pub fn f_internal_add_relu(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_internal_add_relu_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_add_relu_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_add_relu_scalar<S>(
&self,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_add_relu_scalar_<S>(
&mut self,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_internal_aminmax(&self) -> Result<(Tensor, Tensor), TchError>
pub fn f_internal_aminmax_dim(
&self,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_internal_amp_update_scale_(
&mut self,
growth_tracker: &Tensor,
found_inf: &Tensor,
scale_growth_factor: f64,
scale_backoff_factor: f64,
growth_interval: i64
) -> Result<Tensor, TchError>
pub fn f_internal_autocast_to_full_precision(
&self,
cuda_enabled: bool,
cpu_enabled: bool
) -> Result<Tensor, TchError>
pub fn f_internal_autocast_to_reduced_precision(
&self,
cuda_enabled: bool,
cpu_enabled: bool,
cuda_dtype: Kind,
cpu_dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_internal_cast_byte(
&self,
non_blocking: bool
) -> Result<Tensor, TchError>
pub fn f_internal_cast_char(
&self,
non_blocking: bool
) -> Result<Tensor, TchError>
pub fn f_internal_cast_double(
&self,
non_blocking: bool
) -> Result<Tensor, TchError>
pub fn f_internal_cast_float(
&self,
non_blocking: bool
) -> Result<Tensor, TchError>
pub fn f_internal_cast_half(
&self,
non_blocking: bool
) -> Result<Tensor, TchError>
pub fn f_internal_cast_int(
&self,
non_blocking: bool
) -> Result<Tensor, TchError>
pub fn f_internal_cast_long(
&self,
non_blocking: bool
) -> Result<Tensor, TchError>
pub fn f_internal_cast_short(
&self,
non_blocking: bool
) -> Result<Tensor, TchError>
pub fn f_internal_cholesky_solve_helper(
&self,
a: &Tensor,
upper: bool
) -> Result<Tensor, TchError>
pub fn f_internal_coalesce(&self) -> Result<Tensor, TchError>
pub fn f_internal_coalesced_(
&mut self,
coalesced: bool
) -> Result<Tensor, TchError>
pub fn f_internal_compute_linear_combination(
&self,
coefficients: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_compute_linear_combination_out(
&self,
out: &Tensor,
coefficients: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_conj(&self) -> Result<Tensor, TchError>
pub fn f_internal_conj_physical(&self) -> Result<Tensor, TchError>
pub fn f_internal_conv_depthwise2d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_internal_conv_depthwise2d_out<T>(
&self,
out: &Tensor,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_internal_convert_indices_from_coo_to_csr(
&self,
size: i64,
out_int32: bool
) -> Result<Tensor, TchError>
pub fn f_internal_convert_indices_from_coo_to_csr_out(
&self,
out: &Tensor,
size: i64,
out_int32: bool
) -> Result<Tensor, TchError>
pub fn f_internal_convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
transposed: bool,
output_padding: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool,
cudnn_enabled: bool,
allow_tf32: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_internal_convolution_deprecated<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
transposed: bool,
output_padding: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool,
cudnn_enabled: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_internal_convolution_mode<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &str,
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_internal_copy_from(
&self,
dst: &Tensor,
non_blocking: bool
) -> Result<Tensor, TchError>
pub fn f_internal_copy_from_and_resize(
&self,
dst: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_cudnn_rnn<T>(
&self,
weight: &[T],
weight_stride0: i64,
weight_buf: Option<T>,
hx: &Tensor,
cx: Option<T>,
mode: i64,
hidden_size: i64,
proj_size: i64,
num_layers: i64,
batch_first: bool,
dropout: f64,
train: bool,
bidirectional: bool,
batch_sizes: &[i64],
dropout_state: Option<T>
) -> Result<(Tensor, Tensor, Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_internal_debug_has_internal_overlap(&self) -> Result<i64, TchError>
pub fn f_internal_det_lu_based_helper(
&self
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_internal_det_lu_based_helper_backward_helper(
&self,
det_grad: &Tensor,
det: &Tensor,
lu: &Tensor,
pivs: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_dimi(&self) -> Result<i64, TchError>
pub fn f_internal_dimv(&self) -> Result<i64, TchError>
pub fn f_internal_fake_quantize_learnable_per_channel_affine(
&self,
scale: &Tensor,
zero_point: &Tensor,
axis: i64,
quant_min: i64,
quant_max: i64,
grad_factor: f64
) -> Result<Tensor, TchError>
pub fn f_internal_fake_quantize_learnable_per_channel_affine_backward(
&self,
grad: &Tensor,
scale: &Tensor,
zero_point: &Tensor,
axis: i64,
quant_min: i64,
quant_max: i64,
grad_factor: f64
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_internal_fake_quantize_learnable_per_tensor_affine(
&self,
scale: &Tensor,
zero_point: &Tensor,
quant_min: i64,
quant_max: i64,
grad_factor: f64
) -> Result<Tensor, TchError>
pub fn f_internal_fake_quantize_learnable_per_tensor_affine_backward(
&self,
grad: &Tensor,
scale: &Tensor,
zero_point: &Tensor,
quant_min: i64,
quant_max: i64,
grad_factor: f64
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_internal_fake_quantize_per_tensor_affine_cachemask_tensor_qparams(
&self,
scale: &Tensor,
zero_point: &Tensor,
fake_quant_enabled: &Tensor,
quant_min: i64,
quant_max: i64
) -> Result<(Tensor, Tensor), TchError>
pub fn f_internal_fft_c2c(
&self,
dim: &[i64],
normalization: i64,
forward: bool
) -> Result<Tensor, TchError>
pub fn f_internal_fft_c2c_out(
&self,
out: &Tensor,
dim: &[i64],
normalization: i64,
forward: bool
) -> Result<Tensor, TchError>
pub fn f_internal_fft_c2r(
&self,
dim: &[i64],
normalization: i64,
last_dim_size: i64
) -> Result<Tensor, TchError>
pub fn f_internal_fft_c2r_out(
&self,
out: &Tensor,
dim: &[i64],
normalization: i64,
last_dim_size: i64
) -> Result<Tensor, TchError>
pub fn f_internal_fft_r2c(
&self,
dim: &[i64],
normalization: i64,
onesided: bool
) -> Result<Tensor, TchError>
pub fn f_internal_fft_r2c_out(
&self,
out: &Tensor,
dim: &[i64],
normalization: i64,
onesided: bool
) -> Result<Tensor, TchError>
pub fn f_internal_fused_dropout(
&self,
p: f64
) -> Result<(Tensor, Tensor), TchError>
pub fn f_internal_fused_moving_avg_obs_fq_helper(
&self,
observer_on: &Tensor,
fake_quant_on: &Tensor,
running_min: &Tensor,
running_max: &Tensor,
scale: &Tensor,
zero_point: &Tensor,
averaging_const: f64,
quant_min: i64,
quant_max: i64,
ch_axis: i64,
per_row_fake_quant: bool,
symmetric_quant: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_internal_fw_primal(&self, level: i64) -> Result<Tensor, TchError>
pub fn f_internal_gather_sparse_backward(
&self,
dim: i64,
index: &Tensor,
grad: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_grid_sampler_2d_cpu_fallback(
&self,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> Result<Tensor, TchError>
pub fn f_internal_grid_sampler_2d_cpu_fallback_backward(
&self,
grad_output: &Tensor,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_internal_has_compatible_shallow_copy_type(
&self,
from: &Tensor
) -> Result<bool, TchError>
pub fn f_internal_has_same_storage_numel(
&self,
other: &Tensor
) -> Result<bool, TchError>
pub fn f_internal_histogramdd_bin_edges<T>(
&self,
bins: &[i64],
range: &[f64],
weight: Option<T>,
density: bool
) -> Result<Vec<Tensor, Global>, TchError> where
T: Borrow<Tensor>,
pub fn f_internal_histogramdd_from_bin_cts<T>(
&self,
bins: &[i64],
range: &[f64],
weight: Option<T>,
density: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_internal_histogramdd_from_bin_tensors<T>(
&self,
bins: &[T],
weight: Option<T>,
density: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_internal_index_copy_(
&mut self,
dim: i64,
index: &Tensor,
source: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_index_put_impl_<T>(
&mut self,
indices: &[Option<T>],
values: &Tensor,
accumulate: bool,
unsafe_: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_internal_indices(&self) -> Result<Tensor, TchError>
pub fn f_internal_is_zerotensor(&self) -> Result<bool, TchError>
pub fn f_internal_linalg_inv_out_helper_(
&mut self,
infos_lu: &Tensor,
infos_getri: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_linalg_qr_helper(
&self,
mode: &str
) -> Result<(Tensor, Tensor), TchError>
pub fn f_internal_log_softmax(
&self,
dim: i64,
half_to_float: bool
) -> Result<Tensor, TchError>
pub fn f_internal_log_softmax_out(
&self,
out: &Tensor,
dim: i64,
half_to_float: bool
) -> Result<Tensor, TchError>
pub fn f_internal_logcumsumexp(&self, dim: i64) -> Result<Tensor, TchError>
pub fn f_internal_logcumsumexp_out(
&self,
out: &Tensor,
dim: i64
) -> Result<Tensor, TchError>
pub fn f_internal_lu_with_info(
&self,
pivot: bool,
check_errors: bool
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_internal_make_per_channel_quantized_tensor(
&self,
scale: &Tensor,
zero_point: &Tensor,
axis: i64
) -> Result<Tensor, TchError>
pub fn f_internal_make_per_tensor_quantized_tensor(
&self,
scale: f64,
zero_point: i64
) -> Result<Tensor, TchError>
pub fn f_internal_masked_scale(
&self,
mask: &Tensor,
scale: f64
) -> Result<Tensor, TchError>
pub fn f_internal_masked_softmax(
&self,
mask: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_mkldnn_reshape(
&self,
shape: &[i64]
) -> Result<Tensor, TchError>
pub fn f_internal_mkldnn_transpose(
&self,
dim0: i64,
dim1: i64
) -> Result<Tensor, TchError>
pub fn f_internal_mkldnn_transpose_(
&mut self,
dim0: i64,
dim1: i64
) -> Result<Tensor, TchError>
pub fn f_internal_neg_view(&self) -> Result<Tensor, TchError>
pub fn f_internal_new_zeros_with_same_feature_meta(
&self,
other: &Tensor,
self_num_batch_dims: i64
) -> Result<Tensor, TchError>
pub fn f_internal_nnpack_spatial_convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
padding: &[i64],
stride: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_internal_nnz(&self) -> Result<i64, TchError>
pub fn f_internal_pack_padded_sequence(
&self,
lengths: &Tensor,
batch_first: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_internal_pdist_backward(
&self,
grad: &Tensor,
p: f64,
pdist: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_pin_memory(&self, device: Device) -> Result<Tensor, TchError>
pub fn f_internal_remove_batch_dim(
&self,
level: i64,
batch_size: i64,
out_dim: i64
) -> Result<Tensor, TchError>
pub fn f_internal_reshape_alias(
&self,
size: &[i64],
stride: &[i64]
) -> Result<Tensor, TchError>
pub fn f_internal_reshape_from_tensor(
&self,
shape: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_s_where(
&self,
condition: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_sample_dirichlet(&self) -> Result<Tensor, TchError>
pub fn f_internal_shape_as_tensor(&self) -> Result<Tensor, TchError>
pub fn f_internal_slow_conv2d_backward(
&self,
grad_input: &Tensor,
grad_weight: &Tensor,
grad_bias: &Tensor,
grad_output: &Tensor,
weight: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64]
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_internal_sobol_engine_ff_(
&mut self,
n: i64,
sobolstate: &Tensor,
dimension: i64,
num_generated: i64
) -> Result<Tensor, TchError>
pub fn f_internal_sobol_engine_initialize_state_(
&mut self,
dimension: i64
) -> Result<Tensor, TchError>
pub fn f_internal_sobol_engine_scramble_(
&mut self,
ltm: &Tensor,
dimension: i64
) -> Result<Tensor, TchError>
pub fn f_internal_softmax(
&self,
dim: i64,
half_to_float: bool
) -> Result<Tensor, TchError>
pub fn f_internal_softmax_out(
&self,
out: &Tensor,
dim: i64,
half_to_float: bool
) -> Result<Tensor, TchError>
pub fn f_internal_solve_helper(
&self,
a: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_internal_sparse_addmm(
&self,
sparse: &Tensor,
dense: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_sparse_broadcast_to(
&self,
size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_internal_sparse_log_softmax(
&self,
dim: i64,
half_to_float: bool
) -> Result<Tensor, TchError>
pub fn f_internal_sparse_log_softmax_backward_data(
&self,
grad_output: &Tensor,
output: &Tensor,
dim: i64
) -> Result<Tensor, TchError>
pub fn f_internal_sparse_log_softmax_int(
&self,
dim: i64,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_internal_sparse_softmax(
&self,
dim: i64,
half_to_float: bool
) -> Result<Tensor, TchError>
pub fn f_internal_sparse_softmax_backward_data(
&self,
grad_output: &Tensor,
output: &Tensor,
dim: i64
) -> Result<Tensor, TchError>
pub fn f_internal_sparse_softmax_int(
&self,
dim: i64,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_internal_sparse_sparse_matmul(
&self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_sparse_sum(&self) -> Result<Tensor, TchError>
pub fn f_internal_sparse_sum_backward(
&self,
grad: &Tensor,
dim: &[i64]
) -> Result<Tensor, TchError>
pub fn f_internal_sparse_sum_dim(&self, dim: &[i64]) -> Result<Tensor, TchError>
pub fn f_internal_sparse_sum_dim_dtype(
&self,
dim: &[i64],
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_internal_sparse_sum_dtype(
&self,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_internal_standard_gamma(&self) -> Result<Tensor, TchError>
pub fn f_internal_standard_gamma_grad(
&self,
output: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_symeig_helper(
&self,
eigenvectors: bool,
upper: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_internal_test_serialization_subcmul(
&self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_internal_test_warn_in_autograd(&self) -> Result<Tensor, TchError>
pub fn f_internal_to_copy(
&self,
options: (Kind, Device),
non_blocking: bool
) -> Result<Tensor, TchError>
pub fn f_internal_torch_cuda_cu_linker_symbol_op(
&self
) -> Result<Tensor, TchError>
pub fn f_internal_unique(
&self,
sorted: bool,
return_inverse: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_internal_unique2(
&self,
sorted: bool,
return_inverse: bool,
return_counts: bool
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_internal_unsafe_view(&self, size: &[i64]) -> Result<Tensor, TchError>
pub fn f_internal_upsample_bicubic2d_aa(
&self,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_bicubic2d_aa_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_bicubic2d_aa_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_bilinear2d_aa(
&self,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_bilinear2d_aa_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_bilinear2d_aa_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_nearest_exact1d(
&self,
output_size: &[i64],
scales: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_nearest_exact1d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_nearest_exact1d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_nearest_exact2d(
&self,
output_size: &[i64],
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_nearest_exact2d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_nearest_exact2d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_nearest_exact3d(
&self,
output_size: &[i64],
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_nearest_exact3d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_internal_upsample_nearest_exact3d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_internal_values(&self) -> Result<Tensor, TchError>
pub fn f_internal_version(&self) -> Result<i64, TchError>
pub fn f_abs(&self) -> Result<Tensor, TchError>
pub fn f_abs_(&mut self) -> Result<Tensor, TchError>
pub fn f_abs_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_absolute(&self) -> Result<Tensor, TchError>
pub fn f_absolute_(&mut self) -> Result<Tensor, TchError>
pub fn f_absolute_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_acos(&self) -> Result<Tensor, TchError>
pub fn f_acos_(&mut self) -> Result<Tensor, TchError>
pub fn f_acos_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_acosh(&self) -> Result<Tensor, TchError>
pub fn f_acosh_(&mut self) -> Result<Tensor, TchError>
pub fn f_acosh_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_adaptive_avg_pool1d(
&self,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_adaptive_avg_pool2d(
&self,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_adaptive_avg_pool2d_out(
&self,
out: &Tensor,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_adaptive_avg_pool3d(
&self,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_adaptive_avg_pool3d_backward(
&self,
grad_input: &Tensor,
grad_output: &Tensor
) -> Result<Tensor, TchError>
pub fn f_adaptive_avg_pool3d_out(
&self,
out: &Tensor,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_adaptive_max_pool1d(
&self,
output_size: &[i64]
) -> Result<(Tensor, Tensor), TchError>
pub fn f_adaptive_max_pool2d(
&self,
output_size: &[i64]
) -> Result<(Tensor, Tensor), TchError>
pub fn f_adaptive_max_pool2d_backward(
&self,
grad_output: &Tensor,
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_adaptive_max_pool2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_adaptive_max_pool2d_out(
&self,
out: &Tensor,
indices: &Tensor,
output_size: &[i64]
) -> Result<(Tensor, Tensor), TchError>
pub fn f_adaptive_max_pool3d(
&self,
output_size: &[i64]
) -> Result<(Tensor, Tensor), TchError>
pub fn f_adaptive_max_pool3d_backward(
&self,
grad_output: &Tensor,
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_adaptive_max_pool3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_adaptive_max_pool3d_out(
&self,
out: &Tensor,
indices: &Tensor,
output_size: &[i64]
) -> Result<(Tensor, Tensor), TchError>
pub fn f_add(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_add_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_add_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_add_scalar<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_add_scalar_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_addbmm(
&self,
batch1: &Tensor,
batch2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addbmm_(
&mut self,
batch1: &Tensor,
batch2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addbmm_out(
&self,
out: &Tensor,
batch1: &Tensor,
batch2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addcdiv(
&self,
tensor1: &Tensor,
tensor2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addcdiv_(
&mut self,
tensor1: &Tensor,
tensor2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addcdiv_out(
&self,
out: &Tensor,
tensor1: &Tensor,
tensor2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addcmul(
&self,
tensor1: &Tensor,
tensor2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addcmul_(
&mut self,
tensor1: &Tensor,
tensor2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addcmul_out(
&self,
out: &Tensor,
tensor1: &Tensor,
tensor2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addmm(&self, mat1: &Tensor, mat2: &Tensor) -> Result<Tensor, TchError>
pub fn f_addmm_(
&mut self,
mat1: &Tensor,
mat2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addmm_out(
&self,
out: &Tensor,
mat1: &Tensor,
mat2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addmv(&self, mat: &Tensor, vec: &Tensor) -> Result<Tensor, TchError>
pub fn f_addmv_(
&mut self,
mat: &Tensor,
vec: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addmv_out(
&self,
out: &Tensor,
mat: &Tensor,
vec: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addr(&self, vec1: &Tensor, vec2: &Tensor) -> Result<Tensor, TchError>
pub fn f_addr_(
&mut self,
vec1: &Tensor,
vec2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_addr_out(
&self,
out: &Tensor,
vec1: &Tensor,
vec2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_adjoint(&self) -> Result<Tensor, TchError>
pub fn f_alias(&self) -> Result<Tensor, TchError>
pub fn f_align_as(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_all(&self) -> Result<Tensor, TchError>
pub fn f_all_all_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_all_dim(&self, dim: i64, keepdim: bool) -> Result<Tensor, TchError>
pub fn f_all_out(
&self,
out: &Tensor,
dim: i64,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_allclose(
&self,
other: &Tensor,
rtol: f64,
atol: f64,
equal_nan: bool
) -> Result<bool, TchError>
pub fn f_alpha_dropout(&self, p: f64, train: bool) -> Result<Tensor, TchError>
pub fn f_alpha_dropout_(
&mut self,
p: f64,
train: bool
) -> Result<Tensor, TchError>
pub fn f_amax(&self, dim: &[i64], keepdim: bool) -> Result<Tensor, TchError>
pub fn f_amax_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_amin(&self, dim: &[i64], keepdim: bool) -> Result<Tensor, TchError>
pub fn f_amin_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_aminmax(
&self,
dim: impl Into<Option<i64>>,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_aminmax_out(
&self,
min: &Tensor,
max: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_angle(&self) -> Result<Tensor, TchError>
pub fn f_angle_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_any(&self) -> Result<Tensor, TchError>
pub fn f_any_all_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_any_dim(&self, dim: i64, keepdim: bool) -> Result<Tensor, TchError>
pub fn f_any_out(
&self,
out: &Tensor,
dim: i64,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_arccos(&self) -> Result<Tensor, TchError>
pub fn f_arccos_(&mut self) -> Result<Tensor, TchError>
pub fn f_arccos_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_arccosh(&self) -> Result<Tensor, TchError>
pub fn f_arccosh_(&mut self) -> Result<Tensor, TchError>
pub fn f_arccosh_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_arcsin(&self) -> Result<Tensor, TchError>
pub fn f_arcsin_(&mut self) -> Result<Tensor, TchError>
pub fn f_arcsin_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_arcsinh(&self) -> Result<Tensor, TchError>
pub fn f_arcsinh_(&mut self) -> Result<Tensor, TchError>
pub fn f_arcsinh_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_arctan(&self) -> Result<Tensor, TchError>
pub fn f_arctan2(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_arctan2_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_arctan2_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_arctan_(&mut self) -> Result<Tensor, TchError>
pub fn f_arctan_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_arctanh(&self) -> Result<Tensor, TchError>
pub fn f_arctanh_(&mut self) -> Result<Tensor, TchError>
pub fn f_arctanh_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_argmax(
&self,
dim: impl Into<Option<i64>>,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_argmax_out(
&self,
out: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_argmin(
&self,
dim: impl Into<Option<i64>>,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_argmin_out(
&self,
out: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_argsort(&self, dim: i64, descending: bool) -> Result<Tensor, TchError>
pub fn f_argwhere(&self) -> Result<Tensor, TchError>
pub fn f_as_strided(
&self,
size: &[i64],
stride: &[i64],
storage_offset: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_as_strided_(
&mut self,
size: &[i64],
stride: &[i64],
storage_offset: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_asin(&self) -> Result<Tensor, TchError>
pub fn f_asin_(&mut self) -> Result<Tensor, TchError>
pub fn f_asin_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_asinh(&self) -> Result<Tensor, TchError>
pub fn f_asinh_(&mut self) -> Result<Tensor, TchError>
pub fn f_asinh_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_atan(&self) -> Result<Tensor, TchError>
pub fn f_atan2(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_atan2_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_atan2_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_atan_(&mut self) -> Result<Tensor, TchError>
pub fn f_atan_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_atanh(&self) -> Result<Tensor, TchError>
pub fn f_atanh_(&mut self) -> Result<Tensor, TchError>
pub fn f_atanh_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_atleast_1d(&self) -> Result<Tensor, TchError>
pub fn f_atleast_2d(&self) -> Result<Tensor, TchError>
pub fn f_atleast_3d(&self) -> Result<Tensor, TchError>
pub fn f_avg_pool1d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool
) -> Result<Tensor, TchError>
pub fn f_avg_pool2d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_avg_pool2d_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_avg_pool2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_avg_pool2d_out(
&self,
out: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_avg_pool3d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_avg_pool3d_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_avg_pool3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_avg_pool3d_out(
&self,
out: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_baddbmm(
&self,
batch1: &Tensor,
batch2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_baddbmm_(
&mut self,
batch1: &Tensor,
batch2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_baddbmm_out(
&self,
out: &Tensor,
batch1: &Tensor,
batch2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_batch_norm<T>(
&self,
weight: Option<T>,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
training: bool,
momentum: f64,
eps: f64,
cudnn_enabled: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_batch_norm_backward_elemt<T>(
&self,
grad_out: &Tensor,
mean: &Tensor,
invstd: &Tensor,
weight: Option<T>,
mean_dy: &Tensor,
mean_dy_xmu: &Tensor,
count: &Tensor
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_batch_norm_backward_reduce<T>(
&self,
grad_out: &Tensor,
mean: &Tensor,
invstd: &Tensor,
weight: Option<T>,
input_g: bool,
weight_g: bool,
bias_g: bool
) -> Result<(Tensor, Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_batch_norm_elemt<T>(
&self,
weight: Option<T>,
bias: Option<T>,
mean: &Tensor,
invstd: &Tensor,
eps: f64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_batch_norm_elemt_out<T>(
&self,
out: &Tensor,
weight: Option<T>,
bias: Option<T>,
mean: &Tensor,
invstd: &Tensor,
eps: f64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_batch_norm_gather_stats<T>(
&self,
mean: &Tensor,
invstd: &Tensor,
running_mean: Option<T>,
running_var: Option<T>,
momentum: f64,
eps: f64,
count: i64
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_batch_norm_gather_stats_with_counts<T>(
&self,
mean: &Tensor,
invstd: &Tensor,
running_mean: Option<T>,
running_var: Option<T>,
momentum: f64,
eps: f64,
counts: &Tensor
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_batch_norm_stats(&self, eps: f64) -> Result<(Tensor, Tensor), TchError>
pub fn f_batch_norm_update_stats<T>(
&self,
running_mean: Option<T>,
running_var: Option<T>,
momentum: f64
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_bernoulli(&self) -> Result<Tensor, TchError>
pub fn f_bernoulli_(&mut self, p: &Tensor) -> Result<Tensor, TchError>
pub fn f_bernoulli_float_(&mut self, p: f64) -> Result<Tensor, TchError>
pub fn f_bernoulli_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_bernoulli_p(&self, p: f64) -> Result<Tensor, TchError>
pub fn f_binary_cross_entropy<T>(
&self,
target: &Tensor,
weight: Option<T>,
reduction: Reduction
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_binary_cross_entropy_backward<T>(
&self,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_binary_cross_entropy_backward_grad_input<T>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_binary_cross_entropy_out<T>(
&self,
out: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_binary_cross_entropy_with_logits<T>(
&self,
target: &Tensor,
weight: Option<T>,
pos_weight: Option<T>,
reduction: Reduction
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_binary_cross_entropy_with_logits_backward<T>(
&self,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
pos_weight: Option<T>,
reduction: Reduction
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_bincount<T>(
&self,
weights: Option<T>,
minlength: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_bitwise_and<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_and_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_and_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_and_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_bitwise_and_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_bitwise_and_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_bitwise_left_shift(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_bitwise_left_shift_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_bitwise_left_shift_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_bitwise_left_shift_tensor_scalar<S>(
&self,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_left_shift_tensor_scalar_<S>(
&mut self,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_left_shift_tensor_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_not(&self) -> Result<Tensor, TchError>
pub fn f_bitwise_not_(&mut self) -> Result<Tensor, TchError>
pub fn f_bitwise_not_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_bitwise_or<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_or_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_or_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_or_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_bitwise_or_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_bitwise_or_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_bitwise_right_shift(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_bitwise_right_shift_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_bitwise_right_shift_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_bitwise_right_shift_tensor_scalar<S>(
&self,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_right_shift_tensor_scalar_<S>(
&mut self,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_right_shift_tensor_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_xor<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_xor_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_xor_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_bitwise_xor_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_bitwise_xor_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_bitwise_xor_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_bmm(&self, mat2: &Tensor) -> Result<Tensor, TchError>
pub fn f_bmm_out(&self, out: &Tensor, mat2: &Tensor) -> Result<Tensor, TchError>
pub fn f_broadcast_to(&self, size: &[i64]) -> Result<Tensor, TchError>
pub fn f_bucketize(
&self,
boundaries: &Tensor,
out_int32: bool,
right: bool
) -> Result<Tensor, TchError>
pub fn f_bucketize_tensor_out(
&self,
out: &Tensor,
boundaries: &Tensor,
out_int32: bool,
right: bool
) -> Result<Tensor, TchError>
pub fn f_cauchy_(&mut self, median: f64, sigma: f64) -> Result<Tensor, TchError>
pub fn f_ceil(&self) -> Result<Tensor, TchError>
pub fn f_ceil_(&mut self) -> Result<Tensor, TchError>
pub fn f_ceil_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_celu(&self) -> Result<Tensor, TchError>
pub fn f_celu_(&mut self) -> Result<Tensor, TchError>
pub fn f_channel_shuffle(&self, groups: i64) -> Result<Tensor, TchError>
pub fn f_cholesky(&self, upper: bool) -> Result<Tensor, TchError>
pub fn f_cholesky_inverse(&self, upper: bool) -> Result<Tensor, TchError>
pub fn f_cholesky_inverse_out(
&self,
out: &Tensor,
upper: bool
) -> Result<Tensor, TchError>
pub fn f_cholesky_out(
&self,
out: &Tensor,
upper: bool
) -> Result<Tensor, TchError>
pub fn f_cholesky_solve(
&self,
input2: &Tensor,
upper: bool
) -> Result<Tensor, TchError>
pub fn f_cholesky_solve_out(
&self,
out: &Tensor,
input2: &Tensor,
upper: bool
) -> Result<Tensor, TchError>
pub fn f_choose_qparams_optimized(
&self,
numel: i64,
n_bins: i64,
ratio: f64,
bit_width: i64
) -> Result<(Tensor, Tensor), TchError>
pub fn f_chunk(
&self,
chunks: i64,
dim: i64
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_clamp<S>(&self, min: S, max: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clamp_<S>(&mut self, min: S, max: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clamp_max<S>(&self, max: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clamp_max_<S>(&mut self, max: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clamp_max_out<S>(
&self,
out: &Tensor,
max: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clamp_max_tensor(&self, max: &Tensor) -> Result<Tensor, TchError>
pub fn f_clamp_max_tensor_(&mut self, max: &Tensor) -> Result<Tensor, TchError>
pub fn f_clamp_max_tensor_out(
&self,
out: &Tensor,
max: &Tensor
) -> Result<Tensor, TchError>
pub fn f_clamp_min<S>(&self, min: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clamp_min_<S>(&mut self, min: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clamp_min_out<S>(
&self,
out: &Tensor,
min: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clamp_min_tensor(&self, min: &Tensor) -> Result<Tensor, TchError>
pub fn f_clamp_min_tensor_(&mut self, min: &Tensor) -> Result<Tensor, TchError>
pub fn f_clamp_min_tensor_out(
&self,
out: &Tensor,
min: &Tensor
) -> Result<Tensor, TchError>
pub fn f_clamp_out<S>(
&self,
out: &Tensor,
min: S,
max: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clamp_tensor<T>(
&self,
min: Option<T>,
max: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_clamp_tensor_<T>(
&mut self,
min: Option<T>,
max: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_clamp_tensor_out<T>(
&self,
out: &Tensor,
min: Option<T>,
max: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_clip<S>(&self, min: S, max: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clip_<S>(&mut self, min: S, max: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clip_out<S>(
&self,
out: &Tensor,
min: S,
max: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_clip_tensor<T>(
&self,
min: Option<T>,
max: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_clip_tensor_<T>(
&mut self,
min: Option<T>,
max: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_clip_tensor_out<T>(
&self,
out: &Tensor,
min: Option<T>,
max: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_coalesce(&self) -> Result<Tensor, TchError>
pub fn f_col2im(
&self,
output_size: &[i64],
kernel_size: &[i64],
dilation: &[i64],
padding: &[i64],
stride: &[i64]
) -> Result<Tensor, TchError>
pub fn f_col2im_out(
&self,
out: &Tensor,
output_size: &[i64],
kernel_size: &[i64],
dilation: &[i64],
padding: &[i64],
stride: &[i64]
) -> Result<Tensor, TchError>
pub fn f_col_indices(&self) -> Result<Tensor, TchError>
pub fn f_combinations(
&self,
r: i64,
with_replacement: bool
) -> Result<Tensor, TchError>
pub fn f_conj(&self) -> Result<Tensor, TchError>
pub fn f_conj_physical(&self) -> Result<Tensor, TchError>
pub fn f_conj_physical_(&mut self) -> Result<Tensor, TchError>
pub fn f_conj_physical_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_constant_pad_nd(&self, pad: &[i64]) -> Result<Tensor, TchError>
pub fn f_contiguous(&self) -> Result<Tensor, TchError>
pub fn f_conv1d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_conv1d_padding<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &str,
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_conv2d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_conv2d_padding<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &str,
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_conv3d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_conv3d_padding<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &str,
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_conv_depthwise3d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_conv_tbc(
&self,
weight: &Tensor,
bias: &Tensor,
pad: i64
) -> Result<Tensor, TchError>
pub fn f_conv_tbc_backward(
&self,
input: &Tensor,
weight: &Tensor,
bias: &Tensor,
pad: i64
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_conv_transpose1d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
groups: i64,
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_conv_transpose2d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
groups: i64,
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_conv_transpose3d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
groups: i64,
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
transposed: bool,
output_padding: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_convolution_overrideable<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
transposed: bool,
output_padding: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_copy_sparse_to_sparse_(
&mut self,
src: &Tensor,
non_blocking: bool
) -> Result<Tensor, TchError>
pub fn f_copysign(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_copysign_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_copysign_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_copysign_scalar<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_copysign_scalar_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_copysign_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_corrcoef(&self) -> Result<Tensor, TchError>
pub fn f_cos(&self) -> Result<Tensor, TchError>
pub fn f_cos_(&mut self) -> Result<Tensor, TchError>
pub fn f_cos_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_cosh(&self) -> Result<Tensor, TchError>
pub fn f_cosh_(&mut self) -> Result<Tensor, TchError>
pub fn f_cosh_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_count_nonzero(
&self,
dim: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_count_nonzero_dim_intlist(
&self,
dim: &[i64]
) -> Result<Tensor, TchError>
pub fn f_cov<T>(
&self,
correction: i64,
fweights: Option<T>,
aweights: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_cross(
&self,
other: &Tensor,
dim: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_cross_entropy_loss<T>(
&self,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64,
label_smoothing: f64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_cross_out(
&self,
out: &Tensor,
other: &Tensor,
dim: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_crow_indices(&self) -> Result<Tensor, TchError>
pub fn f_cudnn_batch_norm<T>(
&self,
weight: &Tensor,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
training: bool,
exponential_average_factor: f64,
epsilon: f64
) -> Result<(Tensor, Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_cudnn_batch_norm_backward<T>(
&self,
grad_output: &Tensor,
weight: &Tensor,
running_mean: Option<T>,
running_var: Option<T>,
save_mean: Option<T>,
save_var: Option<T>,
epsilon: f64,
reservespace: &Tensor
) -> Result<(Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_cudnn_convolution(
&self,
weight: &Tensor,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool,
allow_tf32: bool
) -> Result<Tensor, TchError>
pub fn f_cudnn_convolution_add_relu<T, S>(
&self,
weight: &Tensor,
z: &Tensor,
alpha: S,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
S: Into<Scalar>,
pub fn f_cudnn_convolution_relu<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_cudnn_convolution_transpose(
&self,
weight: &Tensor,
padding: &[i64],
output_padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool,
allow_tf32: bool
) -> Result<Tensor, TchError>
pub fn f_cudnn_grid_sampler(&self, grid: &Tensor) -> Result<Tensor, TchError>
pub fn f_cudnn_grid_sampler_backward(
&self,
grid: &Tensor,
grad_output: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_cudnn_is_acceptable(&self) -> Result<bool, TchError>
pub fn f_cummax(&self, dim: i64) -> Result<(Tensor, Tensor), TchError>
pub fn f_cummax_out(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64
) -> Result<(Tensor, Tensor), TchError>
pub fn f_cummaxmin_backward(
&self,
grad: &Tensor,
indices: &Tensor,
dim: i64
) -> Result<Tensor, TchError>
pub fn f_cummin(&self, dim: i64) -> Result<(Tensor, Tensor), TchError>
pub fn f_cummin_out(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64
) -> Result<(Tensor, Tensor), TchError>
pub fn f_cumprod(&self, dim: i64, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_cumprod_(&mut self, dim: i64, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_cumprod_backward(
&self,
grad: &Tensor,
dim: i64,
output: &Tensor
) -> Result<Tensor, TchError>
pub fn f_cumprod_out(
&self,
out: &Tensor,
dim: i64,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_cumsum(&self, dim: i64, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_cumsum_(&mut self, dim: i64, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_cumsum_out(
&self,
out: &Tensor,
dim: i64,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_data(&self) -> Result<Tensor, TchError>
pub fn f_deg2rad(&self) -> Result<Tensor, TchError>
pub fn f_deg2rad_(&mut self) -> Result<Tensor, TchError>
pub fn f_deg2rad_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_dense_dim(&self) -> Result<i64, TchError>
pub fn f_dequantize(&self) -> Result<Tensor, TchError>
pub fn f_det(&self) -> Result<Tensor, TchError>
pub fn f_detach(&self) -> Result<Tensor, TchError>
pub fn f_detach_(&mut self) -> Result<Tensor, TchError>
pub fn f_diag(&self, diagonal: i64) -> Result<Tensor, TchError>
pub fn f_diag_embed(
&self,
offset: i64,
dim1: i64,
dim2: i64
) -> Result<Tensor, TchError>
pub fn f_diag_out(
&self,
out: &Tensor,
diagonal: i64
) -> Result<Tensor, TchError>
pub fn f_diagflat(&self, offset: i64) -> Result<Tensor, TchError>
pub fn f_diagonal(
&self,
offset: i64,
dim1: i64,
dim2: i64
) -> Result<Tensor, TchError>
pub fn f_diagonal_scatter(
&self,
src: &Tensor,
offset: i64,
dim1: i64,
dim2: i64
) -> Result<Tensor, TchError>
pub fn f_diff<T>(
&self,
n: i64,
dim: i64,
prepend: Option<T>,
append: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_diff_out<T>(
&self,
out: &Tensor,
n: i64,
dim: i64,
prepend: Option<T>,
append: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_digamma(&self) -> Result<Tensor, TchError>
pub fn f_digamma_(&mut self) -> Result<Tensor, TchError>
pub fn f_digamma_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_dist(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_div(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_div_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_div_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_div_out_mode(
&self,
out: &Tensor,
other: &Tensor,
rounding_mode: &str
) -> Result<Tensor, TchError>
pub fn f_div_scalar<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_div_scalar_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_div_scalar_mode<S>(
&self,
other: S,
rounding_mode: &str
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_div_scalar_mode_<S>(
&mut self,
other: S,
rounding_mode: &str
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_div_tensor_mode(
&self,
other: &Tensor,
rounding_mode: &str
) -> Result<Tensor, TchError>
pub fn f_div_tensor_mode_(
&mut self,
other: &Tensor,
rounding_mode: &str
) -> Result<Tensor, TchError>
pub fn f_divide(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_divide_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_divide_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_divide_out_mode(
&self,
out: &Tensor,
other: &Tensor,
rounding_mode: &str
) -> Result<Tensor, TchError>
pub fn f_divide_scalar<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_divide_scalar_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_divide_scalar_mode<S>(
&self,
other: S,
rounding_mode: &str
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_divide_scalar_mode_<S>(
&mut self,
other: S,
rounding_mode: &str
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_divide_tensor_mode(
&self,
other: &Tensor,
rounding_mode: &str
) -> Result<Tensor, TchError>
pub fn f_divide_tensor_mode_(
&mut self,
other: &Tensor,
rounding_mode: &str
) -> Result<Tensor, TchError>
pub fn f_dot(&self, tensor: &Tensor) -> Result<Tensor, TchError>
pub fn f_dot_out(
&self,
out: &Tensor,
tensor: &Tensor
) -> Result<Tensor, TchError>
pub fn f_dropout(&self, p: f64, train: bool) -> Result<Tensor, TchError>
pub fn f_dropout_(&mut self, p: f64, train: bool) -> Result<Tensor, TchError>
pub fn f_dsplit(&self, sections: i64) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_dsplit_array(
&self,
indices: &[i64]
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_eig(&self, eigenvectors: bool) -> Result<(Tensor, Tensor), TchError>
pub fn f_eig_e(
&self,
e: &Tensor,
v: &Tensor,
eigenvectors: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_elu(&self) -> Result<Tensor, TchError>
pub fn f_elu_(&mut self) -> Result<Tensor, TchError>
pub fn f_elu_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_embedding_renorm_(
&mut self,
indices: &Tensor,
max_norm: f64,
norm_type: f64
) -> Result<Tensor, TchError>
pub fn f_empty_like(&self) -> Result<Tensor, TchError>
pub fn f_eq<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_eq_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_eq_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_eq_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_eq_tensor_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_eq_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_equal(&self, other: &Tensor) -> Result<bool, TchError>
pub fn f_erf(&self) -> Result<Tensor, TchError>
pub fn f_erf_(&mut self) -> Result<Tensor, TchError>
pub fn f_erf_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_erfc(&self) -> Result<Tensor, TchError>
pub fn f_erfc_(&mut self) -> Result<Tensor, TchError>
pub fn f_erfc_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_erfinv(&self) -> Result<Tensor, TchError>
pub fn f_erfinv_(&mut self) -> Result<Tensor, TchError>
pub fn f_erfinv_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_exp(&self) -> Result<Tensor, TchError>
pub fn f_exp2(&self) -> Result<Tensor, TchError>
pub fn f_exp2_(&mut self) -> Result<Tensor, TchError>
pub fn f_exp2_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_exp_(&mut self) -> Result<Tensor, TchError>
pub fn f_exp_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_expand(&self, size: &[i64], implicit: bool) -> Result<Tensor, TchError>
pub fn f_expand_as(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_expm1(&self) -> Result<Tensor, TchError>
pub fn f_expm1_(&mut self) -> Result<Tensor, TchError>
pub fn f_expm1_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_exponential_(&mut self, lambd: f64) -> Result<Tensor, TchError>
pub fn f_fake_quantize_per_channel_affine(
&self,
scale: &Tensor,
zero_point: &Tensor,
axis: i64,
quant_min: i64,
quant_max: i64
) -> Result<Tensor, TchError>
pub fn f_fake_quantize_per_channel_affine_cachemask(
&self,
scale: &Tensor,
zero_point: &Tensor,
axis: i64,
quant_min: i64,
quant_max: i64
) -> Result<(Tensor, Tensor), TchError>
pub fn f_fake_quantize_per_tensor_affine(
&self,
scale: f64,
zero_point: i64,
quant_min: i64,
quant_max: i64
) -> Result<Tensor, TchError>
pub fn f_fake_quantize_per_tensor_affine_cachemask(
&self,
scale: f64,
zero_point: i64,
quant_min: i64,
quant_max: i64
) -> Result<(Tensor, Tensor), TchError>
pub fn f_fake_quantize_per_tensor_affine_tensor_qparams(
&self,
scale: &Tensor,
zero_point: &Tensor,
quant_min: i64,
quant_max: i64
) -> Result<Tensor, TchError>
pub fn f_fbgemm_linear_fp16_weight(
&self,
packed_weight: &Tensor,
bias: &Tensor
) -> Result<Tensor, TchError>
pub fn f_fbgemm_linear_fp16_weight_fp32_activation(
&self,
packed_weight: &Tensor,
bias: &Tensor
) -> Result<Tensor, TchError>
pub fn f_fbgemm_linear_int8_weight<S>(
&self,
weight: &Tensor,
packed: &Tensor,
col_offsets: &Tensor,
weight_scale: S,
weight_zero_point: S,
bias: &Tensor
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_fbgemm_linear_int8_weight_fp32_activation<S>(
&self,
weight: &Tensor,
packed: &Tensor,
col_offsets: &Tensor,
weight_scale: S,
weight_zero_point: S,
bias: &Tensor
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_fbgemm_pack_gemm_matrix_fp16(&self) -> Result<Tensor, TchError>
pub fn f_fbgemm_pack_quantized_matrix(&self) -> Result<Tensor, TchError>
pub fn f_fbgemm_pack_quantized_matrix_kn(
&self,
k: i64,
n: i64
) -> Result<Tensor, TchError>
pub fn f_feature_alpha_dropout(
&self,
p: f64,
train: bool
) -> Result<Tensor, TchError>
pub fn f_feature_alpha_dropout_(
&mut self,
p: f64,
train: bool
) -> Result<Tensor, TchError>
pub fn f_feature_dropout(&self, p: f64, train: bool) -> Result<Tensor, TchError>
pub fn f_feature_dropout_(
&mut self,
p: f64,
train: bool
) -> Result<Tensor, TchError>
pub fn f_fft_fft(
&self,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_fft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_fft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_fft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_fftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_fftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_fftshift<'a>(
&self,
dim: impl Into<Option<&'a [i64]>>
) -> Result<Tensor, TchError>
pub fn f_fft_hfft(
&self,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_hfft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_hfft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_hfft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_hfftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_hfftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ifft(
&self,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ifft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ifft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ifft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ifftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ifftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ifftshift<'a>(
&self,
dim: impl Into<Option<&'a [i64]>>
) -> Result<Tensor, TchError>
pub fn f_fft_ihfft(
&self,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ihfft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ihfft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ihfft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ihfftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_ihfftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_irfft(
&self,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_irfft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_irfft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_irfft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_irfftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_irfftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_rfft(
&self,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_rfft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_rfft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_rfft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_rfftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fft_rfftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Result<Tensor, TchError>
pub fn f_fill_<S>(&mut self, value: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_fill_diagonal_<S>(
&mut self,
fill_value: S,
wrap: bool
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_fill_tensor_(&mut self, value: &Tensor) -> Result<Tensor, TchError>
pub fn f_fix(&self) -> Result<Tensor, TchError>
pub fn f_fix_(&mut self) -> Result<Tensor, TchError>
pub fn f_fix_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_flatten(
&self,
start_dim: i64,
end_dim: i64
) -> Result<Tensor, TchError>
pub fn f_flip(&self, dims: &[i64]) -> Result<Tensor, TchError>
pub fn f_fliplr(&self) -> Result<Tensor, TchError>
pub fn f_flipud(&self) -> Result<Tensor, TchError>
pub fn f_float_power(&self, exponent: &Tensor) -> Result<Tensor, TchError>
pub fn f_float_power_<S>(&mut self, exponent: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_float_power_tensor_(
&mut self,
exponent: &Tensor
) -> Result<Tensor, TchError>
pub fn f_float_power_tensor_scalar<S>(
&self,
exponent: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_float_power_tensor_scalar_out<S>(
&self,
out: &Tensor,
exponent: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_float_power_tensor_tensor_out(
&self,
out: &Tensor,
exponent: &Tensor
) -> Result<Tensor, TchError>
pub fn f_floor(&self) -> Result<Tensor, TchError>
pub fn f_floor_(&mut self) -> Result<Tensor, TchError>
pub fn f_floor_divide(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_floor_divide_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_floor_divide_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_floor_divide_scalar<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_floor_divide_scalar_<S>(
&mut self,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_floor_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_fmax(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_fmax_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_fmin(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_fmin_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_fmod<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_fmod_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_fmod_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_fmod_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_fmod_tensor_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_fmod_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_frac(&self) -> Result<Tensor, TchError>
pub fn f_frac_(&mut self) -> Result<Tensor, TchError>
pub fn f_frac_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_fractional_max_pool2d(
&self,
kernel_size: &[i64],
output_size: &[i64],
random_samples: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_fractional_max_pool2d_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_fractional_max_pool2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_fractional_max_pool2d_output(
&self,
output: &Tensor,
indices: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
random_samples: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_fractional_max_pool3d(
&self,
kernel_size: &[i64],
output_size: &[i64],
random_samples: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_fractional_max_pool3d_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_fractional_max_pool3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_fractional_max_pool3d_output(
&self,
output: &Tensor,
indices: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
random_samples: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_frexp(&self) -> Result<(Tensor, Tensor), TchError>
pub fn f_frexp_tensor_out(
&self,
mantissa: &Tensor,
exponent: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_frobenius_norm(&self) -> Result<Tensor, TchError>
pub fn f_frobenius_norm_dim(
&self,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_frobenius_norm_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_full_like<S>(&self, fill_value: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_fused_moving_avg_obs_fake_quant(
&self,
observer_on: &Tensor,
fake_quant_on: &Tensor,
running_min: &Tensor,
running_max: &Tensor,
scale: &Tensor,
zero_point: &Tensor,
averaging_const: f64,
quant_min: i64,
quant_max: i64,
ch_axis: i64,
per_row_fake_quant: bool,
symmetric_quant: bool
) -> Result<Tensor, TchError>
pub fn f_gather(
&self,
dim: i64,
index: &Tensor,
sparse_grad: bool
) -> Result<Tensor, TchError>
pub fn f_gather_backward(
&self,
grad: &Tensor,
dim: i64,
index: &Tensor,
sparse_grad: bool
) -> Result<Tensor, TchError>
pub fn f_gather_out(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
sparse_grad: bool
) -> Result<Tensor, TchError>
pub fn f_gcd(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_gcd_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_gcd_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_ge<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_ge_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_ge_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_ge_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_ge_tensor_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_ge_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_gelu(&self) -> Result<Tensor, TchError>
pub fn f_gelu_backward(&self, grad: &Tensor) -> Result<Tensor, TchError>
pub fn f_gelu_backward_grad_input(
&self,
grad_input: &Tensor,
grad: &Tensor
) -> Result<Tensor, TchError>
pub fn f_gelu_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_geometric_(&mut self, p: f64) -> Result<Tensor, TchError>
pub fn f_geqrf(&self) -> Result<(Tensor, Tensor), TchError>
pub fn f_geqrf_a(
&self,
a: &Tensor,
tau: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_ger(&self, vec2: &Tensor) -> Result<Tensor, TchError>
pub fn f_ger_out(&self, out: &Tensor, vec2: &Tensor) -> Result<Tensor, TchError>
pub fn f_glu(&self, dim: i64) -> Result<Tensor, TchError>
pub fn f_glu_backward(
&self,
grad_output: &Tensor,
dim: i64
) -> Result<Tensor, TchError>
pub fn f_glu_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
dim: i64
) -> Result<Tensor, TchError>
pub fn f_glu_out(&self, out: &Tensor, dim: i64) -> Result<Tensor, TchError>
pub fn f_grad(&self) -> Result<Tensor, TchError>
pub fn f_greater<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_greater_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_greater_equal<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_greater_equal_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_greater_equal_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_greater_equal_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_greater_equal_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_greater_equal_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_greater_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_greater_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_greater_tensor_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_greater_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_grid_sampler(
&self,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> Result<Tensor, TchError>
pub fn f_grid_sampler_2d(
&self,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> Result<Tensor, TchError>
pub fn f_grid_sampler_3d(
&self,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> Result<Tensor, TchError>
pub fn f_grid_sampler_3d_backward(
&self,
grad_output: &Tensor,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_group_norm<T>(
&self,
num_groups: i64,
weight: Option<T>,
bias: Option<T>,
eps: f64,
cudnn_enabled: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_gru<T>(
&self,
hx: &Tensor,
params: &[T],
has_biases: bool,
num_layers: i64,
dropout: f64,
train: bool,
bidirectional: bool,
batch_first: bool
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_gru_cell<T>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: Option<T>,
b_hh: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_gt<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_gt_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_gt_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_gt_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_gt_tensor_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_gt_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_hardshrink(&self) -> Result<Tensor, TchError>
pub fn f_hardshrink_backward<S>(
&self,
grad_out: &Tensor,
lambd: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_hardshrink_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_out: &Tensor,
lambd: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_hardshrink_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_hardsigmoid(&self) -> Result<Tensor, TchError>
pub fn f_hardsigmoid_(&mut self) -> Result<Tensor, TchError>
pub fn f_hardsigmoid_backward(
&self,
grad_output: &Tensor
) -> Result<Tensor, TchError>
pub fn f_hardsigmoid_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor
) -> Result<Tensor, TchError>
pub fn f_hardsigmoid_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_hardswish(&self) -> Result<Tensor, TchError>
pub fn f_hardswish_(&mut self) -> Result<Tensor, TchError>
pub fn f_hardswish_backward(
&self,
grad_output: &Tensor
) -> Result<Tensor, TchError>
pub fn f_hardswish_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_hardtanh(&self) -> Result<Tensor, TchError>
pub fn f_hardtanh_(&mut self) -> Result<Tensor, TchError>
pub fn f_hardtanh_backward<S>(
&self,
grad_output: &Tensor,
min_val: S,
max_val: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_hardtanh_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
min_val: S,
max_val: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_hardtanh_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_heaviside(&self, values: &Tensor) -> Result<Tensor, TchError>
pub fn f_heaviside_(&mut self, values: &Tensor) -> Result<Tensor, TchError>
pub fn f_heaviside_out(
&self,
out: &Tensor,
values: &Tensor
) -> Result<Tensor, TchError>
pub fn f_hinge_embedding_loss(
&self,
target: &Tensor,
margin: f64,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_histc(&self, bins: i64) -> Result<Tensor, TchError>
pub fn f_histc_out(&self, out: &Tensor, bins: i64) -> Result<Tensor, TchError>
pub fn f_histogram<T>(
&self,
bins: &Tensor,
weight: Option<T>,
density: bool
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_histogram_bin_ct<T>(
&self,
bins: i64,
range: &[f64],
weight: Option<T>,
density: bool
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_histogram_bin_ct_out<T>(
&self,
hist: &Tensor,
bin_edges: &Tensor,
bins: i64,
range: &[f64],
weight: Option<T>,
density: bool
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_histogram_bins_tensor_out<T>(
&self,
hist: &Tensor,
bin_edges: &Tensor,
bins: &Tensor,
weight: Option<T>,
density: bool
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_hsplit(&self, sections: i64) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_hsplit_array(
&self,
indices: &[i64]
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_huber_loss(
&self,
target: &Tensor,
reduction: Reduction,
delta: f64
) -> Result<Tensor, TchError>
pub fn f_huber_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
delta: f64
) -> Result<Tensor, TchError>
pub fn f_huber_loss_backward_out(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
delta: f64
) -> Result<Tensor, TchError>
pub fn f_huber_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction,
delta: f64
) -> Result<Tensor, TchError>
pub fn f_hypot(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_hypot_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_hypot_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_i0(&self) -> Result<Tensor, TchError>
pub fn f_i0_(&mut self) -> Result<Tensor, TchError>
pub fn f_i0_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_igamma(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_igamma_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_igamma_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_igammac(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_igammac_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_igammac_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_im2col(
&self,
kernel_size: &[i64],
dilation: &[i64],
padding: &[i64],
stride: &[i64]
) -> Result<Tensor, TchError>
pub fn f_im2col_out(
&self,
out: &Tensor,
kernel_size: &[i64],
dilation: &[i64],
padding: &[i64],
stride: &[i64]
) -> Result<Tensor, TchError>
pub fn f_imag(&self) -> Result<Tensor, TchError>
pub fn f_index<T>(&self, indices: &[Option<T>]) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_index_add(
&self,
dim: i64,
index: &Tensor,
source: &Tensor
) -> Result<Tensor, TchError>
pub fn f_index_add_(
&mut self,
dim: i64,
index: &Tensor,
source: &Tensor
) -> Result<Tensor, TchError>
pub fn f_index_add_out(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
source: &Tensor
) -> Result<Tensor, TchError>
pub fn f_index_copy(
&self,
dim: i64,
index: &Tensor,
source: &Tensor
) -> Result<Tensor, TchError>
pub fn f_index_copy_(
&mut self,
dim: i64,
index: &Tensor,
source: &Tensor
) -> Result<Tensor, TchError>
pub fn f_index_fill<S>(
&self,
dim: i64,
index: &Tensor,
value: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_index_fill_<S>(
&mut self,
dim: i64,
index: &Tensor,
value: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_index_fill_int_tensor(
&self,
dim: i64,
index: &Tensor,
value: &Tensor
) -> Result<Tensor, TchError>
pub fn f_index_fill_int_tensor_(
&mut self,
dim: i64,
index: &Tensor,
value: &Tensor
) -> Result<Tensor, TchError>
pub fn f_index_put<T>(
&self,
indices: &[Option<T>],
values: &Tensor,
accumulate: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_index_put_<T>(
&mut self,
indices: &[Option<T>],
values: &Tensor,
accumulate: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_index_select(
&self,
dim: i64,
index: &Tensor
) -> Result<Tensor, TchError>
pub fn f_index_select_out(
&self,
out: &Tensor,
dim: i64,
index: &Tensor
) -> Result<Tensor, TchError>
pub fn f_indices(&self) -> Result<Tensor, TchError>
pub fn f_infinitely_differentiable_gelu_backward(
&self,
grad: &Tensor
) -> Result<Tensor, TchError>
pub fn f_inner(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_inner_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_instance_norm<T>(
&self,
weight: Option<T>,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
use_input_stats: bool,
momentum: f64,
eps: f64,
cudnn_enabled: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_int_repr(&self) -> Result<Tensor, TchError>
pub fn f_inverse(&self) -> Result<Tensor, TchError>
pub fn f_inverse_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_is_coalesced(&self) -> Result<bool, TchError>
pub fn f_is_complex(&self) -> Result<bool, TchError>
pub fn f_is_conj(&self) -> Result<bool, TchError>
pub fn f_is_distributed(&self) -> Result<bool, TchError>
pub fn f_is_floating_point(&self) -> Result<bool, TchError>
pub fn f_is_inference(&self) -> Result<bool, TchError>
pub fn f_is_leaf(&self) -> Result<bool, TchError>
pub fn f_is_neg(&self) -> Result<bool, TchError>
pub fn f_is_nonzero(&self) -> Result<bool, TchError>
pub fn f_is_pinned(&self, device: Device) -> Result<bool, TchError>
pub fn f_is_same_size(&self, other: &Tensor) -> Result<bool, TchError>
pub fn f_is_set_to(&self, tensor: &Tensor) -> Result<bool, TchError>
pub fn f_is_signed(&self) -> Result<bool, TchError>
pub fn f_isclose(
&self,
other: &Tensor,
rtol: f64,
atol: f64,
equal_nan: bool
) -> Result<Tensor, TchError>
pub fn f_isfinite(&self) -> Result<Tensor, TchError>
pub fn f_isinf(&self) -> Result<Tensor, TchError>
pub fn f_isnan(&self) -> Result<Tensor, TchError>
pub fn f_isneginf(&self) -> Result<Tensor, TchError>
pub fn f_isneginf_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_isposinf(&self) -> Result<Tensor, TchError>
pub fn f_isposinf_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_isreal(&self) -> Result<Tensor, TchError>
pub fn f_istft<T>(
&self,
n_fft: i64,
hop_length: impl Into<Option<i64>>,
win_length: impl Into<Option<i64>>,
window: Option<T>,
center: bool,
normalized: bool,
onesided: bool,
length: impl Into<Option<i64>>,
return_complex: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_kl_div(
&self,
target: &Tensor,
reduction: Reduction,
log_target: bool
) -> Result<Tensor, TchError>
pub fn f_kl_div_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
log_target: bool
) -> Result<Tensor, TchError>
pub fn f_kron(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_kron_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_kthvalue(
&self,
k: i64,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_kthvalue_values(
&self,
values: &Tensor,
indices: &Tensor,
k: i64,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_l1_loss(
&self,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_l1_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_l1_loss_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_l1_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_layer_norm<T>(
&self,
normalized_shape: &[i64],
weight: Option<T>,
bias: Option<T>,
eps: f64,
cudnn_enable: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_lcm(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_lcm_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_lcm_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_ldexp(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_ldexp_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_ldexp_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_le<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_le_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_le_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_le_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_le_tensor_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_le_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_leaky_relu(&self) -> Result<Tensor, TchError>
pub fn f_leaky_relu_(&mut self) -> Result<Tensor, TchError>
pub fn f_leaky_relu_backward<S>(
&self,
grad_output: &Tensor,
negative_slope: S,
self_is_result: bool
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_leaky_relu_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
negative_slope: S,
self_is_result: bool
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_leaky_relu_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_lerp<S>(&self, end: &Tensor, weight: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_lerp_<S>(
&mut self,
end: &Tensor,
weight: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_lerp_scalar_out<S>(
&self,
out: &Tensor,
end: &Tensor,
weight: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_lerp_tensor(
&self,
end: &Tensor,
weight: &Tensor
) -> Result<Tensor, TchError>
pub fn f_lerp_tensor_(
&mut self,
end: &Tensor,
weight: &Tensor
) -> Result<Tensor, TchError>
pub fn f_lerp_tensor_out(
&self,
out: &Tensor,
end: &Tensor,
weight: &Tensor
) -> Result<Tensor, TchError>
pub fn f_less<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_less_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_less_equal<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_less_equal_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_less_equal_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_less_equal_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_less_equal_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_less_equal_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_less_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_less_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_less_tensor_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_less_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_lgamma(&self) -> Result<Tensor, TchError>
pub fn f_lgamma_(&mut self) -> Result<Tensor, TchError>
pub fn f_lgamma_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_linalg_cholesky(&self, upper: bool) -> Result<Tensor, TchError>
pub fn f_linalg_cholesky_ex(
&self,
upper: bool,
check_errors: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_cholesky_ex_l(
&self,
l: &Tensor,
info: &Tensor,
upper: bool,
check_errors: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_cholesky_out(
&self,
out: &Tensor,
upper: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_cond<S>(&self, p: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_linalg_cond_out<S>(
&self,
out: &Tensor,
p: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_linalg_cond_p_str(&self, p: &str) -> Result<Tensor, TchError>
pub fn f_linalg_cond_p_str_out(
&self,
out: &Tensor,
p: &str
) -> Result<Tensor, TchError>
pub fn f_linalg_cross(
&self,
other: &Tensor,
dim: i64
) -> Result<Tensor, TchError>
pub fn f_linalg_cross_out(
&self,
out: &Tensor,
other: &Tensor,
dim: i64
) -> Result<Tensor, TchError>
pub fn f_linalg_det(&self) -> Result<Tensor, TchError>
pub fn f_linalg_det_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_linalg_eig(&self) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_eig_out(
&self,
eigenvalues: &Tensor,
eigenvectors: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_eigh(&self, uplo: &str) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_eigh_eigvals(
&self,
eigvals: &Tensor,
eigvecs: &Tensor,
uplo: &str
) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_eigvals(&self) -> Result<Tensor, TchError>
pub fn f_linalg_eigvals_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_linalg_eigvalsh(&self, uplo: &str) -> Result<Tensor, TchError>
pub fn f_linalg_eigvalsh_out(
&self,
out: &Tensor,
uplo: &str
) -> Result<Tensor, TchError>
pub fn f_linalg_householder_product(
&self,
tau: &Tensor
) -> Result<Tensor, TchError>
pub fn f_linalg_householder_product_out(
&self,
out: &Tensor,
tau: &Tensor
) -> Result<Tensor, TchError>
pub fn f_linalg_inv(&self) -> Result<Tensor, TchError>
pub fn f_linalg_inv_ex(
&self,
check_errors: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_inv_ex_inverse(
&self,
inverse: &Tensor,
info: &Tensor,
check_errors: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_inv_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_linalg_lstsq(
&self,
b: &Tensor,
rcond: impl Into<Option<f64>>,
driver: &str
) -> Result<(Tensor, Tensor, Tensor, Tensor), TchError>
pub fn f_linalg_lstsq_out(
&self,
solution: &Tensor,
residuals: &Tensor,
rank: &Tensor,
singular_values: &Tensor,
b: &Tensor,
rcond: impl Into<Option<f64>>,
driver: &str
) -> Result<(Tensor, Tensor, Tensor, Tensor), TchError>
pub fn f_linalg_matmul(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_linalg_matmul_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_linalg_matrix_exp(&self) -> Result<Tensor, TchError>
pub fn f_linalg_matrix_power(&self, n: i64) -> Result<Tensor, TchError>
pub fn f_linalg_matrix_power_out(
&self,
out: &Tensor,
n: i64
) -> Result<Tensor, TchError>
pub fn f_linalg_matrix_rank(
&self,
tol: f64,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_matrix_rank_atol_rtol_float(
&self,
atol: impl Into<Option<f64>>,
rtol: impl Into<Option<f64>>,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_matrix_rank_atol_rtol_float_out(
&self,
out: &Tensor,
atol: impl Into<Option<f64>>,
rtol: impl Into<Option<f64>>,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_matrix_rank_atol_rtol_tensor<T>(
&self,
atol: Option<T>,
rtol: Option<T>,
hermitian: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_linalg_matrix_rank_atol_rtol_tensor_out<T>(
&self,
out: &Tensor,
atol: Option<T>,
rtol: Option<T>,
hermitian: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_linalg_matrix_rank_out(
&self,
out: &Tensor,
tol: f64,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_matrix_rank_out_tol_tensor(
&self,
out: &Tensor,
tol: &Tensor,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_matrix_rank_tol_tensor(
&self,
tol: &Tensor,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_norm<'a, S>(
&self,
ord: S,
dim: impl Into<Option<&'a [i64]>>,
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_linalg_norm_ord_str<'a>(
&self,
ord: &str,
dim: impl Into<Option<&'a [i64]>>,
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_linalg_norm_ord_str_out<'a>(
&self,
out: &Tensor,
ord: &str,
dim: impl Into<Option<&'a [i64]>>,
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_linalg_norm_out<'a, S>(
&self,
out: &Tensor,
ord: S,
dim: impl Into<Option<&'a [i64]>>,
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_linalg_pinv(
&self,
rcond: f64,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_pinv_atol_rtol_float(
&self,
atol: impl Into<Option<f64>>,
rtol: impl Into<Option<f64>>,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_pinv_atol_rtol_float_out(
&self,
out: &Tensor,
atol: impl Into<Option<f64>>,
rtol: impl Into<Option<f64>>,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_pinv_atol_rtol_tensor<T>(
&self,
atol: Option<T>,
rtol: Option<T>,
hermitian: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_linalg_pinv_atol_rtol_tensor_out<T>(
&self,
out: &Tensor,
atol: Option<T>,
rtol: Option<T>,
hermitian: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_linalg_pinv_out(
&self,
out: &Tensor,
rcond: f64,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_pinv_out_rcond_tensor(
&self,
out: &Tensor,
rcond: &Tensor,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_pinv_rcond_tensor(
&self,
rcond: &Tensor,
hermitian: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_qr(&self, mode: &str) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_qr_out(
&self,
q: &Tensor,
r: &Tensor,
mode: &str
) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_slogdet(&self) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_slogdet_out(
&self,
sign: &Tensor,
logabsdet: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_linalg_solve(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_linalg_solve_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_linalg_solve_triangular(
&self,
b: &Tensor,
upper: bool,
left: bool,
unitriangular: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_solve_triangular_out(
&self,
out: &Tensor,
b: &Tensor,
upper: bool,
left: bool,
unitriangular: bool
) -> Result<Tensor, TchError>
pub fn f_linalg_tensorinv(&self, ind: i64) -> Result<Tensor, TchError>
pub fn f_linalg_tensorinv_out(
&self,
out: &Tensor,
ind: i64
) -> Result<Tensor, TchError>
pub fn f_linalg_tensorsolve<'a>(
&self,
other: &Tensor,
dims: impl Into<Option<&'a [i64]>>
) -> Result<Tensor, TchError>
pub fn f_linalg_tensorsolve_out<'a>(
&self,
out: &Tensor,
other: &Tensor,
dims: impl Into<Option<&'a [i64]>>
) -> Result<Tensor, TchError>
pub fn f_linear<T>(
&self,
weight: &Tensor,
bias: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_linear_out<T>(
&self,
out: &Tensor,
weight: &Tensor,
bias: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_log(&self) -> Result<Tensor, TchError>
pub fn f_log10(&self) -> Result<Tensor, TchError>
pub fn f_log10_(&mut self) -> Result<Tensor, TchError>
pub fn f_log10_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_log1p(&self) -> Result<Tensor, TchError>
pub fn f_log1p_(&mut self) -> Result<Tensor, TchError>
pub fn f_log1p_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_log2(&self) -> Result<Tensor, TchError>
pub fn f_log2_(&mut self) -> Result<Tensor, TchError>
pub fn f_log2_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_log_(&mut self) -> Result<Tensor, TchError>
pub fn f_log_normal_(&mut self, mean: f64, std: f64) -> Result<Tensor, TchError>
pub fn f_log_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_log_sigmoid(&self) -> Result<Tensor, TchError>
pub fn f_log_sigmoid_backward(
&self,
grad_output: &Tensor,
buffer: &Tensor
) -> Result<Tensor, TchError>
pub fn f_log_sigmoid_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
buffer: &Tensor
) -> Result<Tensor, TchError>
pub fn f_log_sigmoid_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_log_softmax(&self, dim: i64, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_logaddexp(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_logaddexp2(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_logaddexp2_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_logaddexp_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_logcumsumexp(&self, dim: i64) -> Result<Tensor, TchError>
pub fn f_logcumsumexp_out(
&self,
out: &Tensor,
dim: i64
) -> Result<Tensor, TchError>
pub fn f_logdet(&self) -> Result<Tensor, TchError>
pub fn f_logical_and(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_logical_and_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_logical_and_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_logical_not(&self) -> Result<Tensor, TchError>
pub fn f_logical_not_(&mut self) -> Result<Tensor, TchError>
pub fn f_logical_not_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_logical_or(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_logical_or_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_logical_or_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_logical_xor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_logical_xor_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_logical_xor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_logit(&self, eps: impl Into<Option<f64>>) -> Result<Tensor, TchError>
pub fn f_logit_(
&mut self,
eps: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_logit_backward(
&self,
grad_output: &Tensor,
eps: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_logit_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
eps: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_logit_out(
&self,
out: &Tensor,
eps: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_logsumexp(
&self,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_logsumexp_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_lstm<T>(
&self,
hx: &[T],
params: &[T],
has_biases: bool,
num_layers: i64,
dropout: f64,
train: bool,
bidirectional: bool,
batch_first: bool
) -> Result<(Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_lstm_cell<T>(
&self,
hx: &[T],
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: Option<T>,
b_hh: Option<T>
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_lstsq(&self, a: &Tensor) -> Result<(Tensor, Tensor), TchError>
pub fn f_lstsq_x(
&self,
x: &Tensor,
qr: &Tensor,
a: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_lt<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_lt_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_lt_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_lt_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_lt_tensor_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_lt_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_lu_solve(
&self,
lu_data: &Tensor,
lu_pivots: &Tensor
) -> Result<Tensor, TchError>
pub fn f_lu_solve_out(
&self,
out: &Tensor,
lu_data: &Tensor,
lu_pivots: &Tensor
) -> Result<Tensor, TchError>
pub fn f_masked_fill<S>(
&self,
mask: &Tensor,
value: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_masked_fill_<S>(
&mut self,
mask: &Tensor,
value: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_masked_fill_tensor(
&self,
mask: &Tensor,
value: &Tensor
) -> Result<Tensor, TchError>
pub fn f_masked_fill_tensor_(
&mut self,
mask: &Tensor,
value: &Tensor
) -> Result<Tensor, TchError>
pub fn f_masked_scatter(
&self,
mask: &Tensor,
source: &Tensor
) -> Result<Tensor, TchError>
pub fn f_masked_scatter_(
&mut self,
mask: &Tensor,
source: &Tensor
) -> Result<Tensor, TchError>
pub fn f_masked_select(&self, mask: &Tensor) -> Result<Tensor, TchError>
pub fn f_masked_select_backward(
&self,
grad: &Tensor,
mask: &Tensor
) -> Result<Tensor, TchError>
pub fn f_masked_select_out(
&self,
out: &Tensor,
mask: &Tensor
) -> Result<Tensor, TchError>
pub fn f_matmul(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_matmul_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_matrix_exp(&self) -> Result<Tensor, TchError>
pub fn f_matrix_exp_backward(&self, grad: &Tensor) -> Result<Tensor, TchError>
pub fn f_matrix_h(&self) -> Result<Tensor, TchError>
pub fn f_matrix_power(&self, n: i64) -> Result<Tensor, TchError>
pub fn f_matrix_power_out(
&self,
out: &Tensor,
n: i64
) -> Result<Tensor, TchError>
pub fn f_matrix_rank(&self, symmetric: bool) -> Result<Tensor, TchError>
pub fn f_matrix_rank_tol(
&self,
tol: f64,
symmetric: bool
) -> Result<Tensor, TchError>
pub fn f_max(&self) -> Result<Tensor, TchError>
pub fn f_max_dim(
&self,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_max_dim_max(
&self,
max: &Tensor,
max_values: &Tensor,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_max_other(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_max_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_max_pool1d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<Tensor, TchError>
pub fn f_max_pool1d_with_indices(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_max_pool2d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<Tensor, TchError>
pub fn f_max_pool2d_with_indices(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_max_pool2d_with_indices_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool,
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_max_pool2d_with_indices_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool,
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_max_pool2d_with_indices_out(
&self,
out: &Tensor,
indices: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_max_pool3d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<Tensor, TchError>
pub fn f_max_pool3d_with_indices(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_max_pool3d_with_indices_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool,
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_max_pool3d_with_indices_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool,
indices: &Tensor
) -> Result<Tensor, TchError>
pub fn f_max_pool3d_with_indices_out(
&self,
out: &Tensor,
indices: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_max_unpool2d(
&self,
indices: &Tensor,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_max_unpool2d_backward(
&self,
grad_output: &Tensor,
indices: &Tensor,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_max_unpool2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
indices: &Tensor,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_max_unpool2d_out(
&self,
out: &Tensor,
indices: &Tensor,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_max_unpool3d(
&self,
indices: &Tensor,
output_size: &[i64],
stride: &[i64],
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_max_unpool3d_backward(
&self,
grad_output: &Tensor,
indices: &Tensor,
output_size: &[i64],
stride: &[i64],
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_max_unpool3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
indices: &Tensor,
output_size: &[i64],
stride: &[i64],
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_max_unpool3d_out(
&self,
out: &Tensor,
indices: &Tensor,
output_size: &[i64],
stride: &[i64],
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_maximum(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_maximum_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_mean(&self, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_mean_dim(
&self,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_mean_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_median(&self) -> Result<Tensor, TchError>
pub fn f_median_dim(
&self,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_median_dim_values(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_mh(&self) -> Result<Tensor, TchError>
pub fn f_min(&self) -> Result<Tensor, TchError>
pub fn f_min_dim(
&self,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_min_dim_min(
&self,
min: &Tensor,
min_indices: &Tensor,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_min_other(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_min_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_minimum(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_minimum_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_miopen_batch_norm<T>(
&self,
weight: &Tensor,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
training: bool,
exponential_average_factor: f64,
epsilon: f64
) -> Result<(Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_miopen_batch_norm_backward<T>(
&self,
grad_output: &Tensor,
weight: &Tensor,
running_mean: Option<T>,
running_var: Option<T>,
save_mean: Option<T>,
save_var: Option<T>,
epsilon: f64
) -> Result<(Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_miopen_convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_miopen_convolution_transpose<T>(
&self,
weight: &Tensor,
bias: Option<T>,
padding: &[i64],
output_padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_miopen_depthwise_convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_miopen_rnn<T>(
&self,
weight: &[T],
weight_stride0: i64,
hx: &Tensor,
cx: Option<T>,
mode: i64,
hidden_size: i64,
num_layers: i64,
batch_first: bool,
dropout: f64,
train: bool,
bidirectional: bool,
batch_sizes: &[i64],
dropout_state: Option<T>
) -> Result<(Tensor, Tensor, Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_mish(&self) -> Result<Tensor, TchError>
pub fn f_mish_(&mut self) -> Result<Tensor, TchError>
pub fn f_mish_backward(&self, grad_output: &Tensor) -> Result<Tensor, TchError>
pub fn f_mish_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_mkldnn_adaptive_avg_pool2d(
&self,
output_size: &[i64]
) -> Result<Tensor, TchError>
pub fn f_mkldnn_adaptive_avg_pool2d_backward(
&self,
grad_output: &Tensor
) -> Result<Tensor, TchError>
pub fn f_mkldnn_convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_mkldnn_linear<T>(
&self,
weight: &Tensor,
bias: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_mkldnn_linear_backward_weights(
&self,
grad_output: &Tensor,
weight: &Tensor,
bias_defined: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_mkldnn_max_pool2d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<Tensor, TchError>
pub fn f_mkldnn_max_pool2d_backward(
&self,
grad_output: &Tensor,
output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<Tensor, TchError>
pub fn f_mkldnn_max_pool3d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<Tensor, TchError>
pub fn f_mkldnn_max_pool3d_backward(
&self,
grad_output: &Tensor,
output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<Tensor, TchError>
pub fn f_mkldnn_reorder_conv2d_weight(
&self,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError>
pub fn f_mkldnn_reorder_conv3d_weight(
&self,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64
) -> Result<Tensor, TchError>
pub fn f_mm(&self, mat2: &Tensor) -> Result<Tensor, TchError>
pub fn f_mm_out(&self, out: &Tensor, mat2: &Tensor) -> Result<Tensor, TchError>
pub fn f_mode(
&self,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_mode_values(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_moveaxis(
&self,
source: &[i64],
destination: &[i64]
) -> Result<Tensor, TchError>
pub fn f_moveaxis_int(
&self,
source: i64,
destination: i64
) -> Result<Tensor, TchError>
pub fn f_movedim(
&self,
source: &[i64],
destination: &[i64]
) -> Result<Tensor, TchError>
pub fn f_movedim_int(
&self,
source: i64,
destination: i64
) -> Result<Tensor, TchError>
pub fn f_mse_loss(
&self,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_mse_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_mse_loss_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_mse_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_msort(&self) -> Result<Tensor, TchError>
pub fn f_msort_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_mt(&self) -> Result<Tensor, TchError>
pub fn f_mul(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_mul_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_mul_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_mul_scalar<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_mul_scalar_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_multi_margin_loss_backward<T, S>(
&self,
grad_output: &Tensor,
target: &Tensor,
p: S,
margin: S,
weight: Option<T>,
reduction: Reduction
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
S: Into<Scalar>,
pub fn f_multi_margin_loss_backward_grad_input<T, S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
p: S,
margin: S,
weight: Option<T>,
reduction: Reduction
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
S: Into<Scalar>,
pub fn f_multilabel_margin_loss(
&self,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_multilabel_margin_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
is_target: &Tensor
) -> Result<Tensor, TchError>
pub fn f_multilabel_margin_loss_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
is_target: &Tensor
) -> Result<Tensor, TchError>
pub fn f_multilabel_margin_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_multinomial(
&self,
num_samples: i64,
replacement: bool
) -> Result<Tensor, TchError>
pub fn f_multinomial_out(
&self,
out: &Tensor,
num_samples: i64,
replacement: bool
) -> Result<Tensor, TchError>
pub fn f_multiply(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_multiply_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_multiply_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_multiply_scalar<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_multiply_scalar_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_mv(&self, vec: &Tensor) -> Result<Tensor, TchError>
pub fn f_mv_out(&self, out: &Tensor, vec: &Tensor) -> Result<Tensor, TchError>
pub fn f_mvlgamma(&self, p: i64) -> Result<Tensor, TchError>
pub fn f_mvlgamma_(&mut self, p: i64) -> Result<Tensor, TchError>
pub fn f_mvlgamma_out(&self, out: &Tensor, p: i64) -> Result<Tensor, TchError>
pub fn f_nan_to_num(
&self,
nan: impl Into<Option<f64>>,
posinf: impl Into<Option<f64>>,
neginf: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_nan_to_num_(
&mut self,
nan: impl Into<Option<f64>>,
posinf: impl Into<Option<f64>>,
neginf: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_nan_to_num_out(
&self,
out: &Tensor,
nan: impl Into<Option<f64>>,
posinf: impl Into<Option<f64>>,
neginf: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_nanmean(
&self,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_nanmean_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_nanmedian(&self) -> Result<Tensor, TchError>
pub fn f_nanmedian_dim(
&self,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_nanmedian_dim_values(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_nanquantile(
&self,
q: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Result<Tensor, TchError>
pub fn f_nanquantile_out(
&self,
out: &Tensor,
q: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Result<Tensor, TchError>
pub fn f_nanquantile_scalar(
&self,
q: f64,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Result<Tensor, TchError>
pub fn f_nanquantile_scalar_out(
&self,
out: &Tensor,
q: f64,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Result<Tensor, TchError>
pub fn f_nansum(&self, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_nansum_dim_intlist(
&self,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_nansum_intlist_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_narrow(
&self,
dim: i64,
start: i64,
length: i64
) -> Result<Tensor, TchError>
pub fn f_narrow_copy(
&self,
dim: i64,
start: i64,
length: i64
) -> Result<Tensor, TchError>
pub fn f_narrow_copy_out(
&self,
out: &Tensor,
dim: i64,
start: i64,
length: i64
) -> Result<Tensor, TchError>
pub fn f_narrow_tensor(
&self,
dim: i64,
start: &Tensor,
length: i64
) -> Result<Tensor, TchError>
pub fn f_native_batch_norm<T>(
&self,
weight: Option<T>,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
training: bool,
momentum: f64,
eps: f64
) -> Result<(Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_native_batch_norm_out<T>(
&self,
out: &Tensor,
save_mean: &Tensor,
save_invstd: &Tensor,
weight: Option<T>,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
training: bool,
momentum: f64,
eps: f64
) -> Result<(Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_native_channel_shuffle(&self, groups: i64) -> Result<Tensor, TchError>
pub fn f_native_dropout(
&self,
p: f64,
train: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_native_group_norm<T>(
&self,
weight: Option<T>,
bias: Option<T>,
n: i64,
c: i64,
hxw: i64,
group: i64,
eps: f64
) -> Result<(Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_native_layer_norm<T>(
&self,
normalized_shape: &[i64],
weight: Option<T>,
bias: Option<T>,
eps: f64
) -> Result<(Tensor, Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_native_norm(&self) -> Result<Tensor, TchError>
pub fn f_native_norm_scalaropt_dim_dtype<S>(
&self,
p: S,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_ne<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_ne_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_ne_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_ne_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_ne_tensor_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_ne_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_neg(&self) -> Result<Tensor, TchError>
pub fn f_neg_(&mut self) -> Result<Tensor, TchError>
pub fn f_neg_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_negative(&self) -> Result<Tensor, TchError>
pub fn f_negative_(&mut self) -> Result<Tensor, TchError>
pub fn f_negative_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_new_empty(
&self,
size: &[i64],
options: (Kind, Device)
) -> Result<Tensor, TchError>
pub fn f_new_empty_strided(
&self,
size: &[i64],
stride: &[i64],
options: (Kind, Device)
) -> Result<Tensor, TchError>
pub fn f_new_full<S>(
&self,
size: &[i64],
fill_value: S,
options: (Kind, Device)
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_new_ones(
&self,
size: &[i64],
options: (Kind, Device)
) -> Result<Tensor, TchError>
pub fn f_new_zeros(
&self,
size: &[i64],
options: (Kind, Device)
) -> Result<Tensor, TchError>
pub fn f_nextafter(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_nextafter_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_nextafter_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_nll_loss<T>(
&self,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_nll_loss2d<T>(
&self,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_nll_loss2d_backward<T>(
&self,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64,
total_weight: &Tensor
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_nll_loss2d_backward_grad_input<T>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64,
total_weight: &Tensor
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_nll_loss2d_out<T>(
&self,
out: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_nll_loss_backward<T>(
&self,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64,
total_weight: &Tensor
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_nll_loss_backward_grad_input<T>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64,
total_weight: &Tensor
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_nll_loss_nd<T>(
&self,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_nll_loss_out<T>(
&self,
out: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_nonzero(&self) -> Result<Tensor, TchError>
pub fn f_nonzero_numpy(&self) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_nonzero_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_norm(&self) -> Result<Tensor, TchError>
pub fn f_norm_dtype_out<S>(
&self,
out: &Tensor,
p: S,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_norm_out<S>(
&self,
out: &Tensor,
p: S,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_norm_scalaropt_dim<S>(
&self,
p: S,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_norm_scalaropt_dim_dtype<S>(
&self,
p: S,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_norm_scalaropt_dtype<S>(
&self,
p: S,
dtype: Kind
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_normal_(&mut self, mean: f64, std: f64) -> Result<Tensor, TchError>
pub fn f_not_equal<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_not_equal_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_not_equal_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_not_equal_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_not_equal_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_not_equal_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_nuclear_norm(&self, keepdim: bool) -> Result<Tensor, TchError>
pub fn f_nuclear_norm_dim(
&self,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_nuclear_norm_dim_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_nuclear_norm_out(
&self,
out: &Tensor,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_numpy_t(&self) -> Result<Tensor, TchError>
pub fn f_one_hot(&self, num_classes: i64) -> Result<Tensor, TchError>
pub fn f_ones_like(&self) -> Result<Tensor, TchError>
pub fn f_orgqr(&self, input2: &Tensor) -> Result<Tensor, TchError>
pub fn f_orgqr_out(
&self,
out: &Tensor,
input2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_ormqr(
&self,
input2: &Tensor,
input3: &Tensor,
left: bool,
transpose: bool
) -> Result<Tensor, TchError>
pub fn f_ormqr_out(
&self,
out: &Tensor,
input2: &Tensor,
input3: &Tensor,
left: bool,
transpose: bool
) -> Result<Tensor, TchError>
pub fn f_outer(&self, vec2: &Tensor) -> Result<Tensor, TchError>
pub fn f_outer_out(
&self,
out: &Tensor,
vec2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_output_nr(&self) -> Result<i64, TchError>
pub fn f_pdist(&self, p: f64) -> Result<Tensor, TchError>
pub fn f_permute(&self, dims: &[i64]) -> Result<Tensor, TchError>
pub fn f_pin_memory(&self, device: Device) -> Result<Tensor, TchError>
pub fn f_pinverse(&self, rcond: f64) -> Result<Tensor, TchError>
pub fn f_pixel_shuffle(&self, upscale_factor: i64) -> Result<Tensor, TchError>
pub fn f_pixel_unshuffle(
&self,
downscale_factor: i64
) -> Result<Tensor, TchError>
pub fn f_poisson(&self) -> Result<Tensor, TchError>
pub fn f_poisson_nll_loss(
&self,
target: &Tensor,
log_input: bool,
full: bool,
eps: f64,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_polygamma(&self, n: i64) -> Result<Tensor, TchError>
pub fn f_polygamma_(&mut self, n: i64) -> Result<Tensor, TchError>
pub fn f_polygamma_out(&self, out: &Tensor, n: i64) -> Result<Tensor, TchError>
pub fn f_positive(&self) -> Result<Tensor, TchError>
pub fn f_pow(&self, exponent: &Tensor) -> Result<Tensor, TchError>
pub fn f_pow_<S>(&mut self, exponent: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_pow_tensor_(&mut self, exponent: &Tensor) -> Result<Tensor, TchError>
pub fn f_pow_tensor_scalar<S>(&self, exponent: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_pow_tensor_scalar_out<S>(
&self,
out: &Tensor,
exponent: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_pow_tensor_tensor_out(
&self,
out: &Tensor,
exponent: &Tensor
) -> Result<Tensor, TchError>
pub fn f_prelu(&self, weight: &Tensor) -> Result<Tensor, TchError>
pub fn f_prelu_backward(
&self,
grad_output: &Tensor,
weight: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_prod(&self, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_prod_dim_int(
&self,
dim: i64,
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_prod_int_out(
&self,
out: &Tensor,
dim: i64,
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_put(
&self,
index: &Tensor,
source: &Tensor,
accumulate: bool
) -> Result<Tensor, TchError>
pub fn f_put_(
&mut self,
index: &Tensor,
source: &Tensor,
accumulate: bool
) -> Result<Tensor, TchError>
pub fn f_q_per_channel_axis(&self) -> Result<i64, TchError>
pub fn f_q_per_channel_scales(&self) -> Result<Tensor, TchError>
pub fn f_q_per_channel_zero_points(&self) -> Result<Tensor, TchError>
pub fn f_q_scale(&self) -> Result<f64, TchError>
pub fn f_q_zero_point(&self) -> Result<i64, TchError>
pub fn f_qr(&self, some: bool) -> Result<(Tensor, Tensor), TchError>
pub fn f_qr_q(
&self,
q: &Tensor,
r: &Tensor,
some: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_quantile(
&self,
q: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Result<Tensor, TchError>
pub fn f_quantile_out(
&self,
out: &Tensor,
q: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Result<Tensor, TchError>
pub fn f_quantile_scalar(
&self,
q: f64,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Result<Tensor, TchError>
pub fn f_quantile_scalar_out(
&self,
out: &Tensor,
q: f64,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Result<Tensor, TchError>
pub fn f_quantize_per_channel(
&self,
scales: &Tensor,
zero_points: &Tensor,
axis: i64,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_quantize_per_tensor(
&self,
scale: f64,
zero_point: i64,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_quantize_per_tensor_dynamic(
&self,
dtype: Kind,
reduce_range: bool
) -> Result<Tensor, TchError>
pub fn f_quantize_per_tensor_tensor_qparams(
&self,
scale: &Tensor,
zero_point: &Tensor,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_quantized_batch_norm<T>(
&self,
weight: Option<T>,
bias: Option<T>,
mean: &Tensor,
var: &Tensor,
eps: f64,
output_scale: f64,
output_zero_point: i64
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_quantized_gru_cell<S>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: &Tensor,
b_hh: &Tensor,
packed_ih: &Tensor,
packed_hh: &Tensor,
col_offsets_ih: &Tensor,
col_offsets_hh: &Tensor,
scale_ih: S,
scale_hh: S,
zero_point_ih: S,
zero_point_hh: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_quantized_lstm_cell<T, S>(
&self,
hx: &[T],
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: &Tensor,
b_hh: &Tensor,
packed_ih: &Tensor,
packed_hh: &Tensor,
col_offsets_ih: &Tensor,
col_offsets_hh: &Tensor,
scale_ih: S,
scale_hh: S,
zero_point_ih: S,
zero_point_hh: S
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
S: Into<Scalar>,
pub fn f_quantized_max_pool1d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<Tensor, TchError>
pub fn f_quantized_max_pool2d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Result<Tensor, TchError>
pub fn f_quantized_rnn_relu_cell<S>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: &Tensor,
b_hh: &Tensor,
packed_ih: &Tensor,
packed_hh: &Tensor,
col_offsets_ih: &Tensor,
col_offsets_hh: &Tensor,
scale_ih: S,
scale_hh: S,
zero_point_ih: S,
zero_point_hh: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_quantized_rnn_tanh_cell<S>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: &Tensor,
b_hh: &Tensor,
packed_ih: &Tensor,
packed_hh: &Tensor,
col_offsets_ih: &Tensor,
col_offsets_hh: &Tensor,
scale_ih: S,
scale_hh: S,
zero_point_ih: S,
zero_point_hh: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_rad2deg(&self) -> Result<Tensor, TchError>
pub fn f_rad2deg_(&mut self) -> Result<Tensor, TchError>
pub fn f_rad2deg_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_rand_like(&self) -> Result<Tensor, TchError>
pub fn f_randint_like(&self, high: i64) -> Result<Tensor, TchError>
pub fn f_randint_like_low_dtype(
&self,
low: i64,
high: i64
) -> Result<Tensor, TchError>
pub fn f_randn_like(&self) -> Result<Tensor, TchError>
pub fn f_random_(&mut self) -> Result<Tensor, TchError>
pub fn f_random_from_(
&mut self,
from: i64,
to: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_random_to_(&mut self, to: i64) -> Result<Tensor, TchError>
pub fn f_ravel(&self) -> Result<Tensor, TchError>
pub fn f_real(&self) -> Result<Tensor, TchError>
pub fn f_reciprocal(&self) -> Result<Tensor, TchError>
pub fn f_reciprocal_(&mut self) -> Result<Tensor, TchError>
pub fn f_reciprocal_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_reflection_pad1d(&self, padding: &[i64]) -> Result<Tensor, TchError>
pub fn f_reflection_pad1d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_reflection_pad1d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_reflection_pad1d_out(
&self,
out: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_reflection_pad2d(&self, padding: &[i64]) -> Result<Tensor, TchError>
pub fn f_reflection_pad2d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_reflection_pad2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_reflection_pad2d_out(
&self,
out: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_reflection_pad3d(&self, padding: &[i64]) -> Result<Tensor, TchError>
pub fn f_reflection_pad3d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_reflection_pad3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_reflection_pad3d_out(
&self,
out: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_relu(&self) -> Result<Tensor, TchError>
pub fn f_relu6(&self) -> Result<Tensor, TchError>
pub fn f_relu6_(&mut self) -> Result<Tensor, TchError>
pub fn f_relu_(&mut self) -> Result<Tensor, TchError>
pub fn f_remainder<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_remainder_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_remainder_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_remainder_tensor(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_remainder_tensor_(
&mut self,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_remainder_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_renorm<S>(
&self,
p: S,
dim: i64,
maxnorm: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_renorm_<S>(
&mut self,
p: S,
dim: i64,
maxnorm: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_renorm_out<S>(
&self,
out: &Tensor,
p: S,
dim: i64,
maxnorm: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_repeat(&self, repeats: &[i64]) -> Result<Tensor, TchError>
pub fn f_repeat_interleave_self_int(
&self,
repeats: i64,
dim: impl Into<Option<i64>>,
output_size: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_repeat_interleave_self_tensor(
&self,
repeats: &Tensor,
dim: impl Into<Option<i64>>,
output_size: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_replication_pad1d(&self, padding: &[i64]) -> Result<Tensor, TchError>
pub fn f_replication_pad1d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_replication_pad1d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_replication_pad1d_out(
&self,
out: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_replication_pad2d(&self, padding: &[i64]) -> Result<Tensor, TchError>
pub fn f_replication_pad2d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_replication_pad2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_replication_pad2d_out(
&self,
out: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_replication_pad3d(&self, padding: &[i64]) -> Result<Tensor, TchError>
pub fn f_replication_pad3d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_replication_pad3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_replication_pad3d_out(
&self,
out: &Tensor,
padding: &[i64]
) -> Result<Tensor, TchError>
pub fn f_requires_grad_(
&mut self,
requires_grad: bool
) -> Result<Tensor, TchError>
pub fn f_reshape(&self, shape: &[i64]) -> Result<Tensor, TchError>
pub fn f_reshape_as(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_resize_(&mut self, size: &[i64]) -> Result<Tensor, TchError>
pub fn f_resize_as_(
&mut self,
the_template: &Tensor
) -> Result<Tensor, TchError>
pub fn f_resize_as_sparse_(
&mut self,
the_template: &Tensor
) -> Result<Tensor, TchError>
pub fn f_resolve_conj(&self) -> Result<Tensor, TchError>
pub fn f_resolve_neg(&self) -> Result<Tensor, TchError>
pub fn f_retains_grad(&self) -> Result<bool, TchError>
pub fn f_rnn_relu<T>(
&self,
hx: &Tensor,
params: &[T],
has_biases: bool,
num_layers: i64,
dropout: f64,
train: bool,
bidirectional: bool,
batch_first: bool
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_rnn_relu_cell<T>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: Option<T>,
b_hh: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_rnn_tanh<T>(
&self,
hx: &Tensor,
params: &[T],
has_biases: bool,
num_layers: i64,
dropout: f64,
train: bool,
bidirectional: bool,
batch_first: bool
) -> Result<(Tensor, Tensor), TchError> where
T: Borrow<Tensor>,
pub fn f_rnn_tanh_cell<T>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: Option<T>,
b_hh: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_roll(&self, shifts: &[i64], dims: &[i64]) -> Result<Tensor, TchError>
pub fn f_rot90(&self, k: i64, dims: &[i64]) -> Result<Tensor, TchError>
pub fn f_round(&self) -> Result<Tensor, TchError>
pub fn f_round_(&mut self) -> Result<Tensor, TchError>
pub fn f_round_decimals(&self, decimals: i64) -> Result<Tensor, TchError>
pub fn f_round_decimals_(&mut self, decimals: i64) -> Result<Tensor, TchError>
pub fn f_round_decimals_out(
&self,
out: &Tensor,
decimals: i64
) -> Result<Tensor, TchError>
pub fn f_round_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_rrelu(&self, training: bool) -> Result<Tensor, TchError>
pub fn f_rrelu_(&mut self, training: bool) -> Result<Tensor, TchError>
pub fn f_rrelu_with_noise(
&self,
noise: &Tensor,
training: bool
) -> Result<Tensor, TchError>
pub fn f_rrelu_with_noise_(
&mut self,
noise: &Tensor,
training: bool
) -> Result<Tensor, TchError>
pub fn f_rrelu_with_noise_backward<S>(
&self,
grad_output: &Tensor,
noise: &Tensor,
lower: S,
upper: S,
training: bool,
self_is_result: bool
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_rrelu_with_noise_out(
&self,
out: &Tensor,
noise: &Tensor,
training: bool
) -> Result<Tensor, TchError>
pub fn f_rsqrt(&self) -> Result<Tensor, TchError>
pub fn f_rsqrt_(&mut self) -> Result<Tensor, TchError>
pub fn f_rsqrt_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_rsub(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_rsub_scalar<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_scatter(
&self,
dim: i64,
index: &Tensor,
src: &Tensor
) -> Result<Tensor, TchError>
pub fn f_scatter_(
&mut self,
dim: i64,
index: &Tensor,
src: &Tensor
) -> Result<Tensor, TchError>
pub fn f_scatter_add(
&self,
dim: i64,
index: &Tensor,
src: &Tensor
) -> Result<Tensor, TchError>
pub fn f_scatter_add_(
&mut self,
dim: i64,
index: &Tensor,
src: &Tensor
) -> Result<Tensor, TchError>
pub fn f_scatter_add_out(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
src: &Tensor
) -> Result<Tensor, TchError>
pub fn f_scatter_reduce(
&self,
dim: i64,
index: &Tensor,
src: &Tensor,
reduce: &str
) -> Result<Tensor, TchError>
pub fn f_scatter_reduce_(
&mut self,
dim: i64,
index: &Tensor,
src: &Tensor,
reduce: &str
) -> Result<Tensor, TchError>
pub fn f_scatter_reduce_out(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
src: &Tensor,
reduce: &str
) -> Result<Tensor, TchError>
pub fn f_scatter_src_out(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
src: &Tensor
) -> Result<Tensor, TchError>
pub fn f_scatter_value<S>(
&self,
dim: i64,
index: &Tensor,
value: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_scatter_value_<S>(
&mut self,
dim: i64,
index: &Tensor,
value: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_scatter_value_out<S>(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
value: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_scatter_value_reduce<S>(
&self,
dim: i64,
index: &Tensor,
value: S,
reduce: &str
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_scatter_value_reduce_<S>(
&mut self,
dim: i64,
index: &Tensor,
value: S,
reduce: &str
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_scatter_value_reduce_out<S>(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
value: S,
reduce: &str
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_searchsorted<T>(
&self,
sorted_sequence: &Tensor,
out_int32: bool,
right: bool,
side: &str,
sorter: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_searchsorted_tensor_out<T>(
&self,
out: &Tensor,
sorted_sequence: &Tensor,
out_int32: bool,
right: bool,
side: &str,
sorter: Option<T>
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_select(&self, dim: i64, index: i64) -> Result<Tensor, TchError>
pub fn f_select_scatter(
&self,
src: &Tensor,
dim: i64,
index: i64
) -> Result<Tensor, TchError>
pub fn f_selu(&self) -> Result<Tensor, TchError>
pub fn f_selu_(&mut self) -> Result<Tensor, TchError>
pub fn f_set_(&mut self) -> Result<Tensor, TchError>
pub fn f_set_data(&mut self, new_data: &Tensor) -> Result<(), TchError>
pub fn f_set_requires_grad(&self, r: bool) -> Result<Tensor, TchError>
pub fn f_set_source_tensor_(
&mut self,
source: &Tensor
) -> Result<Tensor, TchError>
pub fn f_sgn(&self) -> Result<Tensor, TchError>
pub fn f_sgn_(&mut self) -> Result<Tensor, TchError>
pub fn f_sgn_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_sigmoid(&self) -> Result<Tensor, TchError>
pub fn f_sigmoid_(&mut self) -> Result<Tensor, TchError>
pub fn f_sigmoid_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_sign(&self) -> Result<Tensor, TchError>
pub fn f_sign_(&mut self) -> Result<Tensor, TchError>
pub fn f_sign_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_signbit(&self) -> Result<Tensor, TchError>
pub fn f_signbit_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_silu(&self) -> Result<Tensor, TchError>
pub fn f_silu_(&mut self) -> Result<Tensor, TchError>
pub fn f_silu_backward(&self, grad_output: &Tensor) -> Result<Tensor, TchError>
pub fn f_silu_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor
) -> Result<Tensor, TchError>
pub fn f_silu_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_sin(&self) -> Result<Tensor, TchError>
pub fn f_sin_(&mut self) -> Result<Tensor, TchError>
pub fn f_sin_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_sinc(&self) -> Result<Tensor, TchError>
pub fn f_sinc_(&mut self) -> Result<Tensor, TchError>
pub fn f_sinc_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_sinh(&self) -> Result<Tensor, TchError>
pub fn f_sinh_(&mut self) -> Result<Tensor, TchError>
pub fn f_sinh_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_slice(
&self,
dim: i64,
start: impl Into<Option<i64>>,
end: impl Into<Option<i64>>,
step: i64
) -> Result<Tensor, TchError>
pub fn f_slice_scatter(
&self,
src: &Tensor,
dim: i64,
start: impl Into<Option<i64>>,
end: impl Into<Option<i64>>,
step: i64
) -> Result<Tensor, TchError>
pub fn f_slogdet(&self) -> Result<(Tensor, Tensor), TchError>
pub fn f_slow_conv3d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_slow_conv3d_out<T>(
&self,
out: &Tensor,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_slow_conv_dilated2d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_slow_conv_dilated3d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_slow_conv_transpose2d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_slow_conv_transpose2d_out<T>(
&self,
out: &Tensor,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_slow_conv_transpose3d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_slow_conv_transpose3d_out<T>(
&self,
out: &Tensor,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
dilation: &[i64]
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_smm(&self, mat2: &Tensor) -> Result<Tensor, TchError>
pub fn f_smooth_l1_loss(
&self,
target: &Tensor,
reduction: Reduction,
beta: f64
) -> Result<Tensor, TchError>
pub fn f_smooth_l1_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
beta: f64
) -> Result<Tensor, TchError>
pub fn f_smooth_l1_loss_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
beta: f64
) -> Result<Tensor, TchError>
pub fn f_smooth_l1_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction,
beta: f64
) -> Result<Tensor, TchError>
pub fn f_soft_margin_loss(
&self,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_soft_margin_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_soft_margin_loss_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_soft_margin_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Result<Tensor, TchError>
pub fn f_softmax(&self, dim: i64, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_softplus(&self) -> Result<Tensor, TchError>
pub fn f_softplus_backward<S>(
&self,
grad_output: &Tensor,
beta: S,
threshold: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_softplus_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
beta: S,
threshold: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_softplus_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_softshrink(&self) -> Result<Tensor, TchError>
pub fn f_softshrink_backward<S>(
&self,
grad_output: &Tensor,
lambd: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_softshrink_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
lambd: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_softshrink_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_solve(&self, a: &Tensor) -> Result<(Tensor, Tensor), TchError>
pub fn f_solve_solution(
&self,
solution: &Tensor,
lu: &Tensor,
a: &Tensor
) -> Result<(Tensor, Tensor), TchError>
pub fn f_sort(
&self,
dim: i64,
descending: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_sort_stable(
&self,
stable: bool,
dim: i64,
descending: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_sort_values(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64,
descending: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_sort_values_stable(
&self,
values: &Tensor,
indices: &Tensor,
stable: bool,
dim: i64,
descending: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_sparse_dim(&self) -> Result<i64, TchError>
pub fn f_sparse_mask(&self, mask: &Tensor) -> Result<Tensor, TchError>
pub fn f_sparse_resize_(
&mut self,
size: &[i64],
sparse_dim: i64,
dense_dim: i64
) -> Result<Tensor, TchError>
pub fn f_sparse_resize_and_clear_(
&mut self,
size: &[i64],
sparse_dim: i64,
dense_dim: i64
) -> Result<Tensor, TchError>
pub fn f_sparse_sampled_addmm(
&self,
mat1: &Tensor,
mat2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_sparse_sampled_addmm_out(
&self,
out: &Tensor,
mat1: &Tensor,
mat2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_special_digamma(&self) -> Result<Tensor, TchError>
pub fn f_special_digamma_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_entr(&self) -> Result<Tensor, TchError>
pub fn f_special_entr_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_erf(&self) -> Result<Tensor, TchError>
pub fn f_special_erf_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_erfc(&self) -> Result<Tensor, TchError>
pub fn f_special_erfc_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_erfcx(&self) -> Result<Tensor, TchError>
pub fn f_special_erfcx_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_erfinv(&self) -> Result<Tensor, TchError>
pub fn f_special_erfinv_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_exp2(&self) -> Result<Tensor, TchError>
pub fn f_special_exp2_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_expit(&self) -> Result<Tensor, TchError>
pub fn f_special_expit_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_expm1(&self) -> Result<Tensor, TchError>
pub fn f_special_expm1_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_gammainc(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_gammainc_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_special_gammaincc(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_gammaincc_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_special_gammaln(&self) -> Result<Tensor, TchError>
pub fn f_special_gammaln_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_i0(&self) -> Result<Tensor, TchError>
pub fn f_special_i0_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_i0e(&self) -> Result<Tensor, TchError>
pub fn f_special_i0e_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_i1(&self) -> Result<Tensor, TchError>
pub fn f_special_i1_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_i1e(&self) -> Result<Tensor, TchError>
pub fn f_special_i1e_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_log1p(&self) -> Result<Tensor, TchError>
pub fn f_special_log1p_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_log_softmax(
&self,
dim: i64,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_special_logit(
&self,
eps: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_special_logit_out(
&self,
out: &Tensor,
eps: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_special_logsumexp(
&self,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_special_logsumexp_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_special_multigammaln(&self, p: i64) -> Result<Tensor, TchError>
pub fn f_special_multigammaln_out(
&self,
out: &Tensor,
p: i64
) -> Result<Tensor, TchError>
pub fn f_special_ndtr(&self) -> Result<Tensor, TchError>
pub fn f_special_ndtr_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_ndtri(&self) -> Result<Tensor, TchError>
pub fn f_special_ndtri_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_polygamma(&self, n: i64) -> Result<Tensor, TchError>
pub fn f_special_polygamma_out(
&self,
out: &Tensor,
n: i64
) -> Result<Tensor, TchError>
pub fn f_special_psi(&self) -> Result<Tensor, TchError>
pub fn f_special_psi_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_round(&self, decimals: i64) -> Result<Tensor, TchError>
pub fn f_special_round_out(
&self,
out: &Tensor,
decimals: i64
) -> Result<Tensor, TchError>
pub fn f_special_sinc(&self) -> Result<Tensor, TchError>
pub fn f_special_sinc_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_softmax(
&self,
dim: i64,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_special_xlog1py(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_xlog1py_other_scalar<S>(
&self,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_special_xlog1py_other_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_special_xlog1py_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_special_xlogy(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_xlogy_other_scalar<S>(
&self,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_special_xlogy_other_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_special_xlogy_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_special_zeta(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_special_zeta_other_scalar<S>(
&self,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_special_zeta_other_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_special_zeta_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_split(
&self,
split_size: i64,
dim: i64
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_split_with_sizes(
&self,
split_sizes: &[i64],
dim: i64
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_sqrt(&self) -> Result<Tensor, TchError>
pub fn f_sqrt_(&mut self) -> Result<Tensor, TchError>
pub fn f_sqrt_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_square(&self) -> Result<Tensor, TchError>
pub fn f_square_(&mut self) -> Result<Tensor, TchError>
pub fn f_square_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_squeeze(&self) -> Result<Tensor, TchError>
pub fn f_squeeze_(&mut self) -> Result<Tensor, TchError>
pub fn f_squeeze_dim(&self, dim: i64) -> Result<Tensor, TchError>
pub fn f_squeeze_dim_(&mut self, dim: i64) -> Result<Tensor, TchError>
pub fn f_sspaddmm(
&self,
mat1: &Tensor,
mat2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_sspaddmm_out(
&self,
out: &Tensor,
mat1: &Tensor,
mat2: &Tensor
) -> Result<Tensor, TchError>
pub fn f_std(&self, unbiased: bool) -> Result<Tensor, TchError>
pub fn f_std_correction<'a>(
&self,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_std_correction_out<'a>(
&self,
out: &Tensor,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_std_dim(
&self,
dim: &[i64],
unbiased: bool,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_std_mean(&self, unbiased: bool) -> Result<(Tensor, Tensor), TchError>
pub fn f_std_mean_correction<'a>(
&self,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_std_mean_dim(
&self,
dim: &[i64],
unbiased: bool,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_std_out(
&self,
out: &Tensor,
dim: &[i64],
unbiased: bool,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_stft<T>(
&self,
n_fft: i64,
hop_length: impl Into<Option<i64>>,
win_length: impl Into<Option<i64>>,
window: Option<T>,
normalized: bool,
onesided: bool,
return_complex: bool
) -> Result<Tensor, TchError> where
T: Borrow<Tensor>,
pub fn f_sub(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_sub_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_sub_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_sub_scalar<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_sub_scalar_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_subtract(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_subtract_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_subtract_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_subtract_scalar<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_subtract_scalar_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_sum(&self, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_sum_dim_intlist(
&self,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_sum_intlist_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Result<Tensor, TchError>
pub fn f_sum_to_size(&self, size: &[i64]) -> Result<Tensor, TchError>
pub fn f_svd(
&self,
some: bool,
compute_uv: bool
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_svd_u(
&self,
u: &Tensor,
s: &Tensor,
v: &Tensor,
some: bool,
compute_uv: bool
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_swapaxes(&self, axis0: i64, axis1: i64) -> Result<Tensor, TchError>
pub fn f_swapaxes_(
&mut self,
axis0: i64,
axis1: i64
) -> Result<Tensor, TchError>
pub fn f_swapdims(&self, dim0: i64, dim1: i64) -> Result<Tensor, TchError>
pub fn f_swapdims_(&mut self, dim0: i64, dim1: i64) -> Result<Tensor, TchError>
pub fn f_symeig(
&self,
eigenvectors: bool,
upper: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_symeig_e(
&self,
e: &Tensor,
v: &Tensor,
eigenvectors: bool,
upper: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_tr(&self) -> Result<Tensor, TchError>
pub fn f_t_(&mut self) -> Result<Tensor, TchError>
pub fn f_take(&self, index: &Tensor) -> Result<Tensor, TchError>
pub fn f_take_along_dim(
&self,
indices: &Tensor,
dim: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_take_along_dim_out(
&self,
out: &Tensor,
indices: &Tensor,
dim: impl Into<Option<i64>>
) -> Result<Tensor, TchError>
pub fn f_take_out(
&self,
out: &Tensor,
index: &Tensor
) -> Result<Tensor, TchError>
pub fn f_tan(&self) -> Result<Tensor, TchError>
pub fn f_tan_(&mut self) -> Result<Tensor, TchError>
pub fn f_tan_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_tanh(&self) -> Result<Tensor, TchError>
pub fn f_tanh_(&mut self) -> Result<Tensor, TchError>
pub fn f_tanh_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_tensor_split(
&self,
sections: i64,
dim: i64
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_tensor_split_indices(
&self,
indices: &[i64],
dim: i64
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_tensor_split_tensor_indices_or_sections(
&self,
tensor_indices_or_sections: &Tensor,
dim: i64
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_tensordot(
&self,
other: &Tensor,
dims_self: &[i64],
dims_other: &[i64]
) -> Result<Tensor, TchError>
pub fn f_tensordot_out(
&self,
out: &Tensor,
other: &Tensor,
dims_self: &[i64],
dims_other: &[i64]
) -> Result<Tensor, TchError>
pub fn f_threshold<S>(&self, threshold: S, value: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_threshold_<S>(
&mut self,
threshold: S,
value: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_threshold_backward<S>(
&self,
grad_output: &Tensor,
threshold: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_threshold_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
threshold: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_threshold_out<S>(
&self,
out: &Tensor,
threshold: S,
value: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_tile(&self, dims: &[i64]) -> Result<Tensor, TchError>
pub fn f_to(&self, device: Device) -> Result<Tensor, TchError>
pub fn f_to_dense(&self, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_to_dense_backward(&self, grad: &Tensor) -> Result<Tensor, TchError>
pub fn f_to_device_(
&self,
device: Device,
dtype: Kind,
non_blocking: bool,
copy: bool
) -> Result<Tensor, TchError>
pub fn f_to_dtype(
&self,
dtype: Kind,
non_blocking: bool,
copy: bool
) -> Result<Tensor, TchError>
pub fn f_to_dtype_layout(
&self,
options: (Kind, Device),
non_blocking: bool,
copy: bool
) -> Result<Tensor, TchError>
pub fn f_to_mkldnn(&self, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_to_mkldnn_backward(&self, grad: &Tensor) -> Result<Tensor, TchError>
pub fn f_to_other(
&self,
other: &Tensor,
non_blocking: bool,
copy: bool
) -> Result<Tensor, TchError>
pub fn f_to_sparse(&self) -> Result<Tensor, TchError>
pub fn f_to_sparse_sparse_dim(
&self,
sparse_dim: i64
) -> Result<Tensor, TchError>
pub fn f_topk(
&self,
k: i64,
dim: i64,
largest: bool,
sorted: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_topk_values(
&self,
values: &Tensor,
indices: &Tensor,
k: i64,
dim: i64,
largest: bool,
sorted: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_totype(&self, scalar_type: Kind) -> Result<Tensor, TchError>
pub fn f_trace(&self) -> Result<Tensor, TchError>
pub fn f_transpose(&self, dim0: i64, dim1: i64) -> Result<Tensor, TchError>
pub fn f_transpose_(&mut self, dim0: i64, dim1: i64) -> Result<Tensor, TchError>
pub fn f_triangular_solve(
&self,
a: &Tensor,
upper: bool,
transpose: bool,
unitriangular: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_triangular_solve_x(
&self,
x: &Tensor,
m: &Tensor,
a: &Tensor,
upper: bool,
transpose: bool,
unitriangular: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_tril(&self, diagonal: i64) -> Result<Tensor, TchError>
pub fn f_tril_(&mut self, diagonal: i64) -> Result<Tensor, TchError>
pub fn f_tril_out(
&self,
out: &Tensor,
diagonal: i64
) -> Result<Tensor, TchError>
pub fn f_triu(&self, diagonal: i64) -> Result<Tensor, TchError>
pub fn f_triu_(&mut self, diagonal: i64) -> Result<Tensor, TchError>
pub fn f_triu_out(
&self,
out: &Tensor,
diagonal: i64
) -> Result<Tensor, TchError>
pub fn f_true_divide(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_true_divide_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_true_divide_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_true_divide_scalar<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_true_divide_scalar_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_trunc(&self) -> Result<Tensor, TchError>
pub fn f_trunc_(&mut self) -> Result<Tensor, TchError>
pub fn f_trunc_out(&self, out: &Tensor) -> Result<Tensor, TchError>
pub fn f_type_as(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_unbind(&self, dim: i64) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_unflatten(&self, dim: i64, sizes: &[i64]) -> Result<Tensor, TchError>
pub fn f_unfold(
&self,
dimension: i64,
size: i64,
step: i64
) -> Result<Tensor, TchError>
pub fn f_uniform_(&mut self, from: f64, to: f64) -> Result<Tensor, TchError>
pub fn f_unique_consecutive(
&self,
return_inverse: bool,
return_counts: bool,
dim: impl Into<Option<i64>>
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_unique_dim(
&self,
dim: i64,
sorted: bool,
return_inverse: bool,
return_counts: bool
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_unique_dim_consecutive(
&self,
dim: i64,
return_inverse: bool,
return_counts: bool
) -> Result<(Tensor, Tensor, Tensor), TchError>
pub fn f_unsafe_chunk(
&self,
chunks: i64,
dim: i64
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_unsafe_split(
&self,
split_size: i64,
dim: i64
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_unsafe_split_with_sizes(
&self,
split_sizes: &[i64],
dim: i64
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_unsqueeze(&self, dim: i64) -> Result<Tensor, TchError>
pub fn f_unsqueeze_(&mut self, dim: i64) -> Result<Tensor, TchError>
pub fn f_upsample_bicubic2d(
&self,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_bicubic2d_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_bicubic2d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_upsample_bilinear2d(
&self,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_bilinear2d_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_bilinear2d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_upsample_linear1d(
&self,
output_size: &[i64],
align_corners: bool,
scales: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_linear1d_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_linear1d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_upsample_nearest1d(
&self,
output_size: &[i64],
scales: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_nearest1d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_nearest1d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_upsample_nearest2d(
&self,
output_size: &[i64],
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_nearest2d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_nearest2d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_upsample_nearest3d(
&self,
output_size: &[i64],
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_nearest3d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_nearest3d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_upsample_trilinear3d(
&self,
output_size: &[i64],
align_corners: bool,
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_trilinear3d_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Result<Tensor, TchError>
pub fn f_upsample_trilinear3d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Result<Tensor, TchError>
pub fn f_values(&self) -> Result<Tensor, TchError>
pub fn f_var(&self, unbiased: bool) -> Result<Tensor, TchError>
pub fn f_var_correction<'a>(
&self,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_var_correction_out<'a>(
&self,
out: &Tensor,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_var_dim(
&self,
dim: &[i64],
unbiased: bool,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_var_mean(&self, unbiased: bool) -> Result<(Tensor, Tensor), TchError>
pub fn f_var_mean_correction<'a>(
&self,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_var_mean_dim(
&self,
dim: &[i64],
unbiased: bool,
keepdim: bool
) -> Result<(Tensor, Tensor), TchError>
pub fn f_var_out(
&self,
out: &Tensor,
dim: &[i64],
unbiased: bool,
keepdim: bool
) -> Result<Tensor, TchError>
pub fn f_vdot(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_vdot_out(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_view_(&self, size: &[i64]) -> Result<Tensor, TchError>
pub fn f_view_as(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_view_as_complex(&self) -> Result<Tensor, TchError>
pub fn f_view_as_real(&self) -> Result<Tensor, TchError>
pub fn f_view_dtype(&self, dtype: Kind) -> Result<Tensor, TchError>
pub fn f_vsplit(&self, sections: i64) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_vsplit_array(
&self,
indices: &[i64]
) -> Result<Vec<Tensor, Global>, TchError>
pub fn f_where_scalarother<S>(
&self,
condition: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_where_self(
&self,
condition: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_xlogy(&self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_xlogy_(&mut self, other: &Tensor) -> Result<Tensor, TchError>
pub fn f_xlogy_outscalar_other<S>(
&self,
out: &Tensor,
other: S
) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_xlogy_outtensor(
&self,
out: &Tensor,
other: &Tensor
) -> Result<Tensor, TchError>
pub fn f_xlogy_scalar_other<S>(&self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_xlogy_scalar_other_<S>(&mut self, other: S) -> Result<Tensor, TchError> where
S: Into<Scalar>,
pub fn f_zero_(&mut self) -> Result<Tensor, TchError>
pub fn f_zeros_like(&self) -> Result<Tensor, TchError>
pub fn internal_and_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_and_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn internal_iand_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_iand_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn internal_ilshift_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_ilshift_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn internal_ior_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_ior_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn internal_irshift_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_irshift_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn internal_ixor_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_ixor_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn internal_lshift_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_lshift_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn internal_or_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_or_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn internal_rshift_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_rshift_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn internal_xor_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_xor_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn internal_adaptive_avg_pool2d(&self, output_size: &[i64]) -> Tensor
pub fn internal_adaptive_avg_pool2d_backward(
&self,
grad_output: &Tensor
) -> Tensor
pub fn internal_adaptive_avg_pool3d(&self, output_size: &[i64]) -> Tensor
pub fn internal_adaptive_avg_pool3d_backward(
&self,
grad_output: &Tensor
) -> Tensor
pub fn internal_add_batch_dim(&self, batch_dim: i64, level: i64) -> Tensor
pub fn internal_add_relu(&self, other: &Tensor) -> Tensor
pub fn internal_add_relu_(&mut self, other: &Tensor) -> Tensor
pub fn internal_add_relu_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn internal_add_relu_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_add_relu_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn internal_aminmax(&self) -> (Tensor, Tensor)
pub fn internal_aminmax_dim(&self, dim: i64, keepdim: bool) -> (Tensor, Tensor)
pub fn internal_amp_update_scale_(
&mut self,
growth_tracker: &Tensor,
found_inf: &Tensor,
scale_growth_factor: f64,
scale_backoff_factor: f64,
growth_interval: i64
) -> Tensor
pub fn internal_autocast_to_full_precision(
&self,
cuda_enabled: bool,
cpu_enabled: bool
) -> Tensor
pub fn internal_autocast_to_reduced_precision(
&self,
cuda_enabled: bool,
cpu_enabled: bool,
cuda_dtype: Kind,
cpu_dtype: Kind
) -> Tensor
pub fn internal_cast_byte(&self, non_blocking: bool) -> Tensor
pub fn internal_cast_char(&self, non_blocking: bool) -> Tensor
pub fn internal_cast_double(&self, non_blocking: bool) -> Tensor
pub fn internal_cast_float(&self, non_blocking: bool) -> Tensor
pub fn internal_cast_half(&self, non_blocking: bool) -> Tensor
pub fn internal_cast_int(&self, non_blocking: bool) -> Tensor
pub fn internal_cast_long(&self, non_blocking: bool) -> Tensor
pub fn internal_cast_short(&self, non_blocking: bool) -> Tensor
pub fn internal_cholesky_solve_helper(&self, a: &Tensor, upper: bool) -> Tensor
pub fn internal_coalesce(&self) -> Tensor
pub fn internal_coalesced_(&mut self, coalesced: bool) -> Tensor
pub fn internal_compute_linear_combination(
&self,
coefficients: &Tensor
) -> Tensor
pub fn internal_compute_linear_combination_out(
&self,
out: &Tensor,
coefficients: &Tensor
) -> Tensor
pub fn internal_conj(&self) -> Tensor
pub fn internal_conj_physical(&self) -> Tensor
pub fn internal_conv_depthwise2d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn internal_conv_depthwise2d_out<T>(
&self,
out: &Tensor,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn internal_convert_indices_from_coo_to_csr(
&self,
size: i64,
out_int32: bool
) -> Tensor
pub fn internal_convert_indices_from_coo_to_csr_out(
&self,
out: &Tensor,
size: i64,
out_int32: bool
) -> Tensor
pub fn internal_convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
transposed: bool,
output_padding: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool,
cudnn_enabled: bool,
allow_tf32: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn internal_convolution_deprecated<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
transposed: bool,
output_padding: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool,
cudnn_enabled: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn internal_convolution_mode<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &str,
dilation: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn internal_copy_from(&self, dst: &Tensor, non_blocking: bool) -> Tensor
pub fn internal_copy_from_and_resize(&self, dst: &Tensor) -> Tensor
pub fn internal_cudnn_rnn<T>(
&self,
weight: &[T],
weight_stride0: i64,
weight_buf: Option<T>,
hx: &Tensor,
cx: Option<T>,
mode: i64,
hidden_size: i64,
proj_size: i64,
num_layers: i64,
batch_first: bool,
dropout: f64,
train: bool,
bidirectional: bool,
batch_sizes: &[i64],
dropout_state: Option<T>
) -> (Tensor, Tensor, Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn internal_debug_has_internal_overlap(&self) -> i64
pub fn internal_det_lu_based_helper(&self) -> (Tensor, Tensor, Tensor)
pub fn internal_det_lu_based_helper_backward_helper(
&self,
det_grad: &Tensor,
det: &Tensor,
lu: &Tensor,
pivs: &Tensor
) -> Tensor
pub fn internal_dimi(&self) -> i64
pub fn internal_dimv(&self) -> i64
pub fn internal_fake_quantize_learnable_per_channel_affine(
&self,
scale: &Tensor,
zero_point: &Tensor,
axis: i64,
quant_min: i64,
quant_max: i64,
grad_factor: f64
) -> Tensor
pub fn internal_fake_quantize_learnable_per_channel_affine_backward(
&self,
grad: &Tensor,
scale: &Tensor,
zero_point: &Tensor,
axis: i64,
quant_min: i64,
quant_max: i64,
grad_factor: f64
) -> (Tensor, Tensor, Tensor)
pub fn internal_fake_quantize_learnable_per_tensor_affine(
&self,
scale: &Tensor,
zero_point: &Tensor,
quant_min: i64,
quant_max: i64,
grad_factor: f64
) -> Tensor
pub fn internal_fake_quantize_learnable_per_tensor_affine_backward(
&self,
grad: &Tensor,
scale: &Tensor,
zero_point: &Tensor,
quant_min: i64,
quant_max: i64,
grad_factor: f64
) -> (Tensor, Tensor, Tensor)
pub fn internal_fake_quantize_per_tensor_affine_cachemask_tensor_qparams(
&self,
scale: &Tensor,
zero_point: &Tensor,
fake_quant_enabled: &Tensor,
quant_min: i64,
quant_max: i64
) -> (Tensor, Tensor)
pub fn internal_fft_c2c(
&self,
dim: &[i64],
normalization: i64,
forward: bool
) -> Tensor
pub fn internal_fft_c2c_out(
&self,
out: &Tensor,
dim: &[i64],
normalization: i64,
forward: bool
) -> Tensor
pub fn internal_fft_c2r(
&self,
dim: &[i64],
normalization: i64,
last_dim_size: i64
) -> Tensor
pub fn internal_fft_c2r_out(
&self,
out: &Tensor,
dim: &[i64],
normalization: i64,
last_dim_size: i64
) -> Tensor
pub fn internal_fft_r2c(
&self,
dim: &[i64],
normalization: i64,
onesided: bool
) -> Tensor
pub fn internal_fft_r2c_out(
&self,
out: &Tensor,
dim: &[i64],
normalization: i64,
onesided: bool
) -> Tensor
pub fn internal_fused_dropout(&self, p: f64) -> (Tensor, Tensor)
pub fn internal_fused_moving_avg_obs_fq_helper(
&self,
observer_on: &Tensor,
fake_quant_on: &Tensor,
running_min: &Tensor,
running_max: &Tensor,
scale: &Tensor,
zero_point: &Tensor,
averaging_const: f64,
quant_min: i64,
quant_max: i64,
ch_axis: i64,
per_row_fake_quant: bool,
symmetric_quant: bool
) -> (Tensor, Tensor)
pub fn internal_fw_primal(&self, level: i64) -> Tensor
pub fn internal_gather_sparse_backward(
&self,
dim: i64,
index: &Tensor,
grad: &Tensor
) -> Tensor
pub fn internal_grid_sampler_2d_cpu_fallback(
&self,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> Tensor
pub fn internal_grid_sampler_2d_cpu_fallback_backward(
&self,
grad_output: &Tensor,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> (Tensor, Tensor)
pub fn internal_has_compatible_shallow_copy_type(&self, from: &Tensor) -> bool
pub fn internal_has_same_storage_numel(&self, other: &Tensor) -> bool
pub fn internal_histogramdd_bin_edges<T>(
&self,
bins: &[i64],
range: &[f64],
weight: Option<T>,
density: bool
) -> Vec<Tensor, Global> where
T: Borrow<Tensor>,
pub fn internal_histogramdd_from_bin_cts<T>(
&self,
bins: &[i64],
range: &[f64],
weight: Option<T>,
density: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn internal_histogramdd_from_bin_tensors<T>(
&self,
bins: &[T],
weight: Option<T>,
density: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn internal_index_copy_(
&mut self,
dim: i64,
index: &Tensor,
source: &Tensor
) -> Tensor
pub fn internal_index_put_impl_<T>(
&mut self,
indices: &[Option<T>],
values: &Tensor,
accumulate: bool,
unsafe_: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn internal_indices(&self) -> Tensor
pub fn internal_is_zerotensor(&self) -> bool
pub fn internal_linalg_inv_out_helper_(
&mut self,
infos_lu: &Tensor,
infos_getri: &Tensor
) -> Tensor
pub fn internal_linalg_qr_helper(&self, mode: &str) -> (Tensor, Tensor)
pub fn internal_log_softmax(&self, dim: i64, half_to_float: bool) -> Tensor
pub fn internal_log_softmax_out(
&self,
out: &Tensor,
dim: i64,
half_to_float: bool
) -> Tensor
pub fn internal_logcumsumexp(&self, dim: i64) -> Tensor
pub fn internal_logcumsumexp_out(&self, out: &Tensor, dim: i64) -> Tensor
pub fn internal_lu_with_info(
&self,
pivot: bool,
check_errors: bool
) -> (Tensor, Tensor, Tensor)
pub fn internal_make_per_channel_quantized_tensor(
&self,
scale: &Tensor,
zero_point: &Tensor,
axis: i64
) -> Tensor
pub fn internal_make_per_tensor_quantized_tensor(
&self,
scale: f64,
zero_point: i64
) -> Tensor
pub fn internal_masked_scale(&self, mask: &Tensor, scale: f64) -> Tensor
pub fn internal_masked_softmax(&self, mask: &Tensor) -> Tensor
pub fn internal_mkldnn_reshape(&self, shape: &[i64]) -> Tensor
pub fn internal_mkldnn_transpose(&self, dim0: i64, dim1: i64) -> Tensor
pub fn internal_mkldnn_transpose_(&mut self, dim0: i64, dim1: i64) -> Tensor
pub fn internal_neg_view(&self) -> Tensor
pub fn internal_new_zeros_with_same_feature_meta(
&self,
other: &Tensor,
self_num_batch_dims: i64
) -> Tensor
pub fn internal_nnpack_spatial_convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
padding: &[i64],
stride: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn internal_nnz(&self) -> i64
pub fn internal_pack_padded_sequence(
&self,
lengths: &Tensor,
batch_first: bool
) -> (Tensor, Tensor)
pub fn internal_pdist_backward(
&self,
grad: &Tensor,
p: f64,
pdist: &Tensor
) -> Tensor
pub fn internal_pin_memory(&self, device: Device) -> Tensor
pub fn internal_remove_batch_dim(
&self,
level: i64,
batch_size: i64,
out_dim: i64
) -> Tensor
pub fn internal_reshape_alias(&self, size: &[i64], stride: &[i64]) -> Tensor
pub fn internal_reshape_from_tensor(&self, shape: &Tensor) -> Tensor
pub fn internal_s_where(&self, condition: &Tensor, other: &Tensor) -> Tensor
pub fn internal_sample_dirichlet(&self) -> Tensor
pub fn internal_shape_as_tensor(&self) -> Tensor
pub fn internal_slow_conv2d_backward(
&self,
grad_input: &Tensor,
grad_weight: &Tensor,
grad_bias: &Tensor,
grad_output: &Tensor,
weight: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64]
) -> (Tensor, Tensor, Tensor)
pub fn internal_sobol_engine_ff_(
&mut self,
n: i64,
sobolstate: &Tensor,
dimension: i64,
num_generated: i64
) -> Tensor
pub fn internal_sobol_engine_initialize_state_(
&mut self,
dimension: i64
) -> Tensor
pub fn internal_sobol_engine_scramble_(
&mut self,
ltm: &Tensor,
dimension: i64
) -> Tensor
pub fn internal_softmax(&self, dim: i64, half_to_float: bool) -> Tensor
pub fn internal_softmax_out(
&self,
out: &Tensor,
dim: i64,
half_to_float: bool
) -> Tensor
pub fn internal_solve_helper(&self, a: &Tensor) -> (Tensor, Tensor)
pub fn internal_sparse_addmm(&self, sparse: &Tensor, dense: &Tensor) -> Tensor
pub fn internal_sparse_broadcast_to(&self, size: &[i64]) -> Tensor
pub fn internal_sparse_log_softmax(
&self,
dim: i64,
half_to_float: bool
) -> Tensor
pub fn internal_sparse_log_softmax_backward_data(
&self,
grad_output: &Tensor,
output: &Tensor,
dim: i64
) -> Tensor
pub fn internal_sparse_log_softmax_int(&self, dim: i64, dtype: Kind) -> Tensor
pub fn internal_sparse_softmax(&self, dim: i64, half_to_float: bool) -> Tensor
pub fn internal_sparse_softmax_backward_data(
&self,
grad_output: &Tensor,
output: &Tensor,
dim: i64
) -> Tensor
pub fn internal_sparse_softmax_int(&self, dim: i64, dtype: Kind) -> Tensor
pub fn internal_sparse_sparse_matmul(&self, other: &Tensor) -> Tensor
pub fn internal_sparse_sum(&self) -> Tensor
pub fn internal_sparse_sum_backward(&self, grad: &Tensor, dim: &[i64]) -> Tensor
pub fn internal_sparse_sum_dim(&self, dim: &[i64]) -> Tensor
pub fn internal_sparse_sum_dim_dtype(&self, dim: &[i64], dtype: Kind) -> Tensor
pub fn internal_sparse_sum_dtype(&self, dtype: Kind) -> Tensor
pub fn internal_standard_gamma(&self) -> Tensor
pub fn internal_standard_gamma_grad(&self, output: &Tensor) -> Tensor
pub fn internal_symeig_helper(
&self,
eigenvectors: bool,
upper: bool
) -> (Tensor, Tensor)
pub fn internal_test_serialization_subcmul(&self, other: &Tensor) -> Tensor
pub fn internal_test_warn_in_autograd(&self) -> Tensor
pub fn internal_to_copy(
&self,
options: (Kind, Device),
non_blocking: bool
) -> Tensor
pub fn internal_torch_cuda_cu_linker_symbol_op(&self) -> Tensor
pub fn internal_unique(
&self,
sorted: bool,
return_inverse: bool
) -> (Tensor, Tensor)
pub fn internal_unique2(
&self,
sorted: bool,
return_inverse: bool,
return_counts: bool
) -> (Tensor, Tensor, Tensor)
pub fn internal_unsafe_view(&self, size: &[i64]) -> Tensor
pub fn internal_upsample_bicubic2d_aa(
&self,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn internal_upsample_bicubic2d_aa_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn internal_upsample_bicubic2d_aa_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Tensor
pub fn internal_upsample_bilinear2d_aa(
&self,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn internal_upsample_bilinear2d_aa_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn internal_upsample_bilinear2d_aa_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Tensor
pub fn internal_upsample_nearest_exact1d(
&self,
output_size: &[i64],
scales: impl Into<Option<f64>>
) -> Tensor
pub fn internal_upsample_nearest_exact1d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales: impl Into<Option<f64>>
) -> Tensor
pub fn internal_upsample_nearest_exact1d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Tensor
pub fn internal_upsample_nearest_exact2d(
&self,
output_size: &[i64],
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn internal_upsample_nearest_exact2d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn internal_upsample_nearest_exact2d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Tensor
pub fn internal_upsample_nearest_exact3d(
&self,
output_size: &[i64],
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn internal_upsample_nearest_exact3d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn internal_upsample_nearest_exact3d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Tensor
pub fn internal_values(&self) -> Tensor
pub fn internal_version(&self) -> i64
pub fn abs(&self) -> Tensor
pub fn abs_(&mut self) -> Tensor
pub fn abs_out(&self, out: &Tensor) -> Tensor
pub fn absolute(&self) -> Tensor
pub fn absolute_(&mut self) -> Tensor
pub fn absolute_out(&self, out: &Tensor) -> Tensor
pub fn acos(&self) -> Tensor
pub fn acos_(&mut self) -> Tensor
pub fn acos_out(&self, out: &Tensor) -> Tensor
pub fn acosh(&self) -> Tensor
pub fn acosh_(&mut self) -> Tensor
pub fn acosh_out(&self, out: &Tensor) -> Tensor
pub fn adaptive_avg_pool1d(&self, output_size: &[i64]) -> Tensor
pub fn adaptive_avg_pool2d(&self, output_size: &[i64]) -> Tensor
pub fn adaptive_avg_pool2d_out(
&self,
out: &Tensor,
output_size: &[i64]
) -> Tensor
pub fn adaptive_avg_pool3d(&self, output_size: &[i64]) -> Tensor
pub fn adaptive_avg_pool3d_backward(
&self,
grad_input: &Tensor,
grad_output: &Tensor
) -> Tensor
pub fn adaptive_avg_pool3d_out(
&self,
out: &Tensor,
output_size: &[i64]
) -> Tensor
pub fn adaptive_max_pool1d(&self, output_size: &[i64]) -> (Tensor, Tensor)
pub fn adaptive_max_pool2d(&self, output_size: &[i64]) -> (Tensor, Tensor)
pub fn adaptive_max_pool2d_backward(
&self,
grad_output: &Tensor,
indices: &Tensor
) -> Tensor
pub fn adaptive_max_pool2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
indices: &Tensor
) -> Tensor
pub fn adaptive_max_pool2d_out(
&self,
out: &Tensor,
indices: &Tensor,
output_size: &[i64]
) -> (Tensor, Tensor)
pub fn adaptive_max_pool3d(&self, output_size: &[i64]) -> (Tensor, Tensor)
pub fn adaptive_max_pool3d_backward(
&self,
grad_output: &Tensor,
indices: &Tensor
) -> Tensor
pub fn adaptive_max_pool3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
indices: &Tensor
) -> Tensor
pub fn adaptive_max_pool3d_out(
&self,
out: &Tensor,
indices: &Tensor,
output_size: &[i64]
) -> (Tensor, Tensor)
pub fn g_add(&self, other: &Tensor) -> Tensor
pub fn g_add_(&mut self, other: &Tensor) -> Tensor
pub fn add_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn g_add_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn g_add_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn addbmm(&self, batch1: &Tensor, batch2: &Tensor) -> Tensor
pub fn addbmm_(&mut self, batch1: &Tensor, batch2: &Tensor) -> Tensor
pub fn addbmm_out(
&self,
out: &Tensor,
batch1: &Tensor,
batch2: &Tensor
) -> Tensor
pub fn addcdiv(&self, tensor1: &Tensor, tensor2: &Tensor) -> Tensor
pub fn addcdiv_(&mut self, tensor1: &Tensor, tensor2: &Tensor) -> Tensor
pub fn addcdiv_out(
&self,
out: &Tensor,
tensor1: &Tensor,
tensor2: &Tensor
) -> Tensor
pub fn addcmul(&self, tensor1: &Tensor, tensor2: &Tensor) -> Tensor
pub fn addcmul_(&mut self, tensor1: &Tensor, tensor2: &Tensor) -> Tensor
pub fn addcmul_out(
&self,
out: &Tensor,
tensor1: &Tensor,
tensor2: &Tensor
) -> Tensor
pub fn addmm(&self, mat1: &Tensor, mat2: &Tensor) -> Tensor
pub fn addmm_(&mut self, mat1: &Tensor, mat2: &Tensor) -> Tensor
pub fn addmm_out(&self, out: &Tensor, mat1: &Tensor, mat2: &Tensor) -> Tensor
pub fn addmv(&self, mat: &Tensor, vec: &Tensor) -> Tensor
pub fn addmv_(&mut self, mat: &Tensor, vec: &Tensor) -> Tensor
pub fn addmv_out(&self, out: &Tensor, mat: &Tensor, vec: &Tensor) -> Tensor
pub fn addr(&self, vec1: &Tensor, vec2: &Tensor) -> Tensor
pub fn addr_(&mut self, vec1: &Tensor, vec2: &Tensor) -> Tensor
pub fn addr_out(&self, out: &Tensor, vec1: &Tensor, vec2: &Tensor) -> Tensor
pub fn adjoint(&self) -> Tensor
pub fn alias(&self) -> Tensor
pub fn align_as(&self, other: &Tensor) -> Tensor
pub fn all(&self) -> Tensor
pub fn all_all_out(&self, out: &Tensor) -> Tensor
pub fn all_dim(&self, dim: i64, keepdim: bool) -> Tensor
pub fn all_out(&self, out: &Tensor, dim: i64, keepdim: bool) -> Tensor
pub fn allclose(
&self,
other: &Tensor,
rtol: f64,
atol: f64,
equal_nan: bool
) -> bool
pub fn alpha_dropout(&self, p: f64, train: bool) -> Tensor
pub fn alpha_dropout_(&mut self, p: f64, train: bool) -> Tensor
pub fn amax(&self, dim: &[i64], keepdim: bool) -> Tensor
pub fn amax_out(&self, out: &Tensor, dim: &[i64], keepdim: bool) -> Tensor
pub fn amin(&self, dim: &[i64], keepdim: bool) -> Tensor
pub fn amin_out(&self, out: &Tensor, dim: &[i64], keepdim: bool) -> Tensor
pub fn aminmax(
&self,
dim: impl Into<Option<i64>>,
keepdim: bool
) -> (Tensor, Tensor)
pub fn aminmax_out(
&self,
min: &Tensor,
max: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool
) -> (Tensor, Tensor)
pub fn angle(&self) -> Tensor
pub fn angle_out(&self, out: &Tensor) -> Tensor
pub fn any(&self) -> Tensor
pub fn any_all_out(&self, out: &Tensor) -> Tensor
pub fn any_dim(&self, dim: i64, keepdim: bool) -> Tensor
pub fn any_out(&self, out: &Tensor, dim: i64, keepdim: bool) -> Tensor
pub fn arccos(&self) -> Tensor
pub fn arccos_(&mut self) -> Tensor
pub fn arccos_out(&self, out: &Tensor) -> Tensor
pub fn arccosh(&self) -> Tensor
pub fn arccosh_(&mut self) -> Tensor
pub fn arccosh_out(&self, out: &Tensor) -> Tensor
pub fn arcsin(&self) -> Tensor
pub fn arcsin_(&mut self) -> Tensor
pub fn arcsin_out(&self, out: &Tensor) -> Tensor
pub fn arcsinh(&self) -> Tensor
pub fn arcsinh_(&mut self) -> Tensor
pub fn arcsinh_out(&self, out: &Tensor) -> Tensor
pub fn arctan(&self) -> Tensor
pub fn arctan2(&self, other: &Tensor) -> Tensor
pub fn arctan2_(&mut self, other: &Tensor) -> Tensor
pub fn arctan2_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn arctan_(&mut self) -> Tensor
pub fn arctan_out(&self, out: &Tensor) -> Tensor
pub fn arctanh(&self) -> Tensor
pub fn arctanh_(&mut self) -> Tensor
pub fn arctanh_out(&self, out: &Tensor) -> Tensor
pub fn argmax(&self, dim: impl Into<Option<i64>>, keepdim: bool) -> Tensor
pub fn argmax_out(
&self,
out: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool
) -> Tensor
pub fn argmin(&self, dim: impl Into<Option<i64>>, keepdim: bool) -> Tensor
pub fn argmin_out(
&self,
out: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool
) -> Tensor
pub fn argsort(&self, dim: i64, descending: bool) -> Tensor
pub fn argwhere(&self) -> Tensor
pub fn as_strided(
&self,
size: &[i64],
stride: &[i64],
storage_offset: impl Into<Option<i64>>
) -> Tensor
pub fn as_strided_(
&mut self,
size: &[i64],
stride: &[i64],
storage_offset: impl Into<Option<i64>>
) -> Tensor
pub fn asin(&self) -> Tensor
pub fn asin_(&mut self) -> Tensor
pub fn asin_out(&self, out: &Tensor) -> Tensor
pub fn asinh(&self) -> Tensor
pub fn asinh_(&mut self) -> Tensor
pub fn asinh_out(&self, out: &Tensor) -> Tensor
pub fn atan(&self) -> Tensor
pub fn atan2(&self, other: &Tensor) -> Tensor
pub fn atan2_(&mut self, other: &Tensor) -> Tensor
pub fn atan2_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn atan_(&mut self) -> Tensor
pub fn atan_out(&self, out: &Tensor) -> Tensor
pub fn atanh(&self) -> Tensor
pub fn atanh_(&mut self) -> Tensor
pub fn atanh_out(&self, out: &Tensor) -> Tensor
pub fn atleast_1d(&self) -> Tensor
pub fn atleast_2d(&self) -> Tensor
pub fn atleast_3d(&self) -> Tensor
pub fn avg_pool1d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool
) -> Tensor
pub fn avg_pool2d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Tensor
pub fn avg_pool2d_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Tensor
pub fn avg_pool2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Tensor
pub fn avg_pool2d_out(
&self,
out: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Tensor
pub fn avg_pool3d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Tensor
pub fn avg_pool3d_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Tensor
pub fn avg_pool3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Tensor
pub fn avg_pool3d_out(
&self,
out: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
ceil_mode: bool,
count_include_pad: bool,
divisor_override: impl Into<Option<i64>>
) -> Tensor
pub fn baddbmm(&self, batch1: &Tensor, batch2: &Tensor) -> Tensor
pub fn baddbmm_(&mut self, batch1: &Tensor, batch2: &Tensor) -> Tensor
pub fn baddbmm_out(
&self,
out: &Tensor,
batch1: &Tensor,
batch2: &Tensor
) -> Tensor
pub fn batch_norm<T>(
&self,
weight: Option<T>,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
training: bool,
momentum: f64,
eps: f64,
cudnn_enabled: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn batch_norm_backward_elemt<T>(
&self,
grad_out: &Tensor,
mean: &Tensor,
invstd: &Tensor,
weight: Option<T>,
mean_dy: &Tensor,
mean_dy_xmu: &Tensor,
count: &Tensor
) -> Tensor where
T: Borrow<Tensor>,
pub fn batch_norm_backward_reduce<T>(
&self,
grad_out: &Tensor,
mean: &Tensor,
invstd: &Tensor,
weight: Option<T>,
input_g: bool,
weight_g: bool,
bias_g: bool
) -> (Tensor, Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn batch_norm_elemt<T>(
&self,
weight: Option<T>,
bias: Option<T>,
mean: &Tensor,
invstd: &Tensor,
eps: f64
) -> Tensor where
T: Borrow<Tensor>,
pub fn batch_norm_elemt_out<T>(
&self,
out: &Tensor,
weight: Option<T>,
bias: Option<T>,
mean: &Tensor,
invstd: &Tensor,
eps: f64
) -> Tensor where
T: Borrow<Tensor>,
pub fn batch_norm_gather_stats<T>(
&self,
mean: &Tensor,
invstd: &Tensor,
running_mean: Option<T>,
running_var: Option<T>,
momentum: f64,
eps: f64,
count: i64
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn batch_norm_gather_stats_with_counts<T>(
&self,
mean: &Tensor,
invstd: &Tensor,
running_mean: Option<T>,
running_var: Option<T>,
momentum: f64,
eps: f64,
counts: &Tensor
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn batch_norm_stats(&self, eps: f64) -> (Tensor, Tensor)
pub fn batch_norm_update_stats<T>(
&self,
running_mean: Option<T>,
running_var: Option<T>,
momentum: f64
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn bernoulli(&self) -> Tensor
pub fn bernoulli_(&mut self, p: &Tensor) -> Tensor
pub fn bernoulli_float_(&mut self, p: f64) -> Tensor
pub fn bernoulli_out(&self, out: &Tensor) -> Tensor
pub fn bernoulli_p(&self, p: f64) -> Tensor
pub fn binary_cross_entropy<T>(
&self,
target: &Tensor,
weight: Option<T>,
reduction: Reduction
) -> Tensor where
T: Borrow<Tensor>,
pub fn binary_cross_entropy_backward<T>(
&self,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction
) -> Tensor where
T: Borrow<Tensor>,
pub fn binary_cross_entropy_backward_grad_input<T>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction
) -> Tensor where
T: Borrow<Tensor>,
pub fn binary_cross_entropy_out<T>(
&self,
out: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction
) -> Tensor where
T: Borrow<Tensor>,
pub fn binary_cross_entropy_with_logits<T>(
&self,
target: &Tensor,
weight: Option<T>,
pos_weight: Option<T>,
reduction: Reduction
) -> Tensor where
T: Borrow<Tensor>,
pub fn binary_cross_entropy_with_logits_backward<T>(
&self,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
pos_weight: Option<T>,
reduction: Reduction
) -> Tensor where
T: Borrow<Tensor>,
pub fn bincount<T>(&self, weights: Option<T>, minlength: i64) -> Tensor where
T: Borrow<Tensor>,
pub fn bitwise_and<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_and_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_and_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_and_tensor(&self, other: &Tensor) -> Tensor
pub fn bitwise_and_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn bitwise_and_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn bitwise_left_shift(&self, other: &Tensor) -> Tensor
pub fn bitwise_left_shift_(&mut self, other: &Tensor) -> Tensor
pub fn bitwise_left_shift_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Tensor
pub fn bitwise_left_shift_tensor_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_left_shift_tensor_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_left_shift_tensor_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_not(&self) -> Tensor
pub fn bitwise_not_(&mut self) -> Tensor
pub fn bitwise_not_out(&self, out: &Tensor) -> Tensor
pub fn bitwise_or<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_or_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_or_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_or_tensor(&self, other: &Tensor) -> Tensor
pub fn bitwise_or_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn bitwise_or_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn bitwise_right_shift(&self, other: &Tensor) -> Tensor
pub fn bitwise_right_shift_(&mut self, other: &Tensor) -> Tensor
pub fn bitwise_right_shift_tensor_out(
&self,
out: &Tensor,
other: &Tensor
) -> Tensor
pub fn bitwise_right_shift_tensor_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_right_shift_tensor_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_right_shift_tensor_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_xor<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_xor_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_xor_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn bitwise_xor_tensor(&self, other: &Tensor) -> Tensor
pub fn bitwise_xor_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn bitwise_xor_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn bmm(&self, mat2: &Tensor) -> Tensor
pub fn bmm_out(&self, out: &Tensor, mat2: &Tensor) -> Tensor
pub fn broadcast_to(&self, size: &[i64]) -> Tensor
pub fn bucketize(
&self,
boundaries: &Tensor,
out_int32: bool,
right: bool
) -> Tensor
pub fn bucketize_tensor_out(
&self,
out: &Tensor,
boundaries: &Tensor,
out_int32: bool,
right: bool
) -> Tensor
pub fn cauchy_(&mut self, median: f64, sigma: f64) -> Tensor
pub fn ceil(&self) -> Tensor
pub fn ceil_(&mut self) -> Tensor
pub fn ceil_out(&self, out: &Tensor) -> Tensor
pub fn celu(&self) -> Tensor
pub fn celu_(&mut self) -> Tensor
pub fn channel_shuffle(&self, groups: i64) -> Tensor
pub fn cholesky(&self, upper: bool) -> Tensor
pub fn cholesky_inverse(&self, upper: bool) -> Tensor
pub fn cholesky_inverse_out(&self, out: &Tensor, upper: bool) -> Tensor
pub fn cholesky_out(&self, out: &Tensor, upper: bool) -> Tensor
pub fn cholesky_solve(&self, input2: &Tensor, upper: bool) -> Tensor
pub fn cholesky_solve_out(
&self,
out: &Tensor,
input2: &Tensor,
upper: bool
) -> Tensor
pub fn choose_qparams_optimized(
&self,
numel: i64,
n_bins: i64,
ratio: f64,
bit_width: i64
) -> (Tensor, Tensor)
pub fn chunk(&self, chunks: i64, dim: i64) -> Vec<Tensor, Global>
pub fn clamp<S>(&self, min: S, max: S) -> Tensor where
S: Into<Scalar>,
pub fn clamp_<S>(&mut self, min: S, max: S) -> Tensor where
S: Into<Scalar>,
pub fn clamp_max<S>(&self, max: S) -> Tensor where
S: Into<Scalar>,
pub fn clamp_max_<S>(&mut self, max: S) -> Tensor where
S: Into<Scalar>,
pub fn clamp_max_out<S>(&self, out: &Tensor, max: S) -> Tensor where
S: Into<Scalar>,
pub fn clamp_max_tensor(&self, max: &Tensor) -> Tensor
pub fn clamp_max_tensor_(&mut self, max: &Tensor) -> Tensor
pub fn clamp_max_tensor_out(&self, out: &Tensor, max: &Tensor) -> Tensor
pub fn clamp_min<S>(&self, min: S) -> Tensor where
S: Into<Scalar>,
pub fn clamp_min_<S>(&mut self, min: S) -> Tensor where
S: Into<Scalar>,
pub fn clamp_min_out<S>(&self, out: &Tensor, min: S) -> Tensor where
S: Into<Scalar>,
pub fn clamp_min_tensor(&self, min: &Tensor) -> Tensor
pub fn clamp_min_tensor_(&mut self, min: &Tensor) -> Tensor
pub fn clamp_min_tensor_out(&self, out: &Tensor, min: &Tensor) -> Tensor
pub fn clamp_out<S>(&self, out: &Tensor, min: S, max: S) -> Tensor where
S: Into<Scalar>,
pub fn clamp_tensor<T>(&self, min: Option<T>, max: Option<T>) -> Tensor where
T: Borrow<Tensor>,
pub fn clamp_tensor_<T>(&mut self, min: Option<T>, max: Option<T>) -> Tensor where
T: Borrow<Tensor>,
pub fn clamp_tensor_out<T>(
&self,
out: &Tensor,
min: Option<T>,
max: Option<T>
) -> Tensor where
T: Borrow<Tensor>,
pub fn clip<S>(&self, min: S, max: S) -> Tensor where
S: Into<Scalar>,
pub fn clip_<S>(&mut self, min: S, max: S) -> Tensor where
S: Into<Scalar>,
pub fn clip_out<S>(&self, out: &Tensor, min: S, max: S) -> Tensor where
S: Into<Scalar>,
pub fn clip_tensor<T>(&self, min: Option<T>, max: Option<T>) -> Tensor where
T: Borrow<Tensor>,
pub fn clip_tensor_<T>(&mut self, min: Option<T>, max: Option<T>) -> Tensor where
T: Borrow<Tensor>,
pub fn clip_tensor_out<T>(
&self,
out: &Tensor,
min: Option<T>,
max: Option<T>
) -> Tensor where
T: Borrow<Tensor>,
pub fn coalesce(&self) -> Tensor
pub fn col2im(
&self,
output_size: &[i64],
kernel_size: &[i64],
dilation: &[i64],
padding: &[i64],
stride: &[i64]
) -> Tensor
pub fn col2im_out(
&self,
out: &Tensor,
output_size: &[i64],
kernel_size: &[i64],
dilation: &[i64],
padding: &[i64],
stride: &[i64]
) -> Tensor
pub fn col_indices(&self) -> Tensor
pub fn combinations(&self, r: i64, with_replacement: bool) -> Tensor
pub fn conj(&self) -> Tensor
pub fn conj_physical(&self) -> Tensor
pub fn conj_physical_(&mut self) -> Tensor
pub fn conj_physical_out(&self, out: &Tensor) -> Tensor
pub fn constant_pad_nd(&self, pad: &[i64]) -> Tensor
pub fn contiguous(&self) -> Tensor
pub fn conv1d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn conv1d_padding<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &str,
dilation: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn conv2d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn conv2d_padding<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &str,
dilation: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn conv3d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn conv3d_padding<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &str,
dilation: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn conv_depthwise3d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn conv_tbc(&self, weight: &Tensor, bias: &Tensor, pad: i64) -> Tensor
pub fn conv_tbc_backward(
&self,
input: &Tensor,
weight: &Tensor,
bias: &Tensor,
pad: i64
) -> (Tensor, Tensor, Tensor)
pub fn conv_transpose1d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
groups: i64,
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn conv_transpose2d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
groups: i64,
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn conv_transpose3d<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
groups: i64,
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
transposed: bool,
output_padding: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn convolution_overrideable<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
transposed: bool,
output_padding: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn copy_sparse_to_sparse_(
&mut self,
src: &Tensor,
non_blocking: bool
) -> Tensor
pub fn copysign(&self, other: &Tensor) -> Tensor
pub fn copysign_(&mut self, other: &Tensor) -> Tensor
pub fn copysign_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn copysign_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn copysign_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn copysign_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn corrcoef(&self) -> Tensor
pub fn cos(&self) -> Tensor
pub fn cos_(&mut self) -> Tensor
pub fn cos_out(&self, out: &Tensor) -> Tensor
pub fn cosh(&self) -> Tensor
pub fn cosh_(&mut self) -> Tensor
pub fn cosh_out(&self, out: &Tensor) -> Tensor
pub fn count_nonzero(&self, dim: impl Into<Option<i64>>) -> Tensor
pub fn count_nonzero_dim_intlist(&self, dim: &[i64]) -> Tensor
pub fn cov<T>(
&self,
correction: i64,
fweights: Option<T>,
aweights: Option<T>
) -> Tensor where
T: Borrow<Tensor>,
pub fn cross(&self, other: &Tensor, dim: impl Into<Option<i64>>) -> Tensor
pub fn cross_entropy_loss<T>(
&self,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64,
label_smoothing: f64
) -> Tensor where
T: Borrow<Tensor>,
pub fn cross_out(
&self,
out: &Tensor,
other: &Tensor,
dim: impl Into<Option<i64>>
) -> Tensor
pub fn crow_indices(&self) -> Tensor
pub fn cudnn_batch_norm<T>(
&self,
weight: &Tensor,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
training: bool,
exponential_average_factor: f64,
epsilon: f64
) -> (Tensor, Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn cudnn_batch_norm_backward<T>(
&self,
grad_output: &Tensor,
weight: &Tensor,
running_mean: Option<T>,
running_var: Option<T>,
save_mean: Option<T>,
save_var: Option<T>,
epsilon: f64,
reservespace: &Tensor
) -> (Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn cudnn_convolution(
&self,
weight: &Tensor,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool,
allow_tf32: bool
) -> Tensor
pub fn cudnn_convolution_add_relu<T, S>(
&self,
weight: &Tensor,
z: &Tensor,
alpha: S,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
S: Into<Scalar>,
pub fn cudnn_convolution_relu<T>(
&self,
weight: &Tensor,
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn cudnn_convolution_transpose(
&self,
weight: &Tensor,
padding: &[i64],
output_padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool,
allow_tf32: bool
) -> Tensor
pub fn cudnn_grid_sampler(&self, grid: &Tensor) -> Tensor
pub fn cudnn_grid_sampler_backward(
&self,
grid: &Tensor,
grad_output: &Tensor
) -> (Tensor, Tensor)
pub fn cudnn_is_acceptable(&self) -> bool
pub fn cummax(&self, dim: i64) -> (Tensor, Tensor)
pub fn cummax_out(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64
) -> (Tensor, Tensor)
pub fn cummaxmin_backward(
&self,
grad: &Tensor,
indices: &Tensor,
dim: i64
) -> Tensor
pub fn cummin(&self, dim: i64) -> (Tensor, Tensor)
pub fn cummin_out(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64
) -> (Tensor, Tensor)
pub fn cumprod(&self, dim: i64, dtype: Kind) -> Tensor
pub fn cumprod_(&mut self, dim: i64, dtype: Kind) -> Tensor
pub fn cumprod_backward(
&self,
grad: &Tensor,
dim: i64,
output: &Tensor
) -> Tensor
pub fn cumprod_out(&self, out: &Tensor, dim: i64, dtype: Kind) -> Tensor
pub fn cumsum(&self, dim: i64, dtype: Kind) -> Tensor
pub fn cumsum_(&mut self, dim: i64, dtype: Kind) -> Tensor
pub fn cumsum_out(&self, out: &Tensor, dim: i64, dtype: Kind) -> Tensor
pub fn data(&self) -> Tensor
pub fn deg2rad(&self) -> Tensor
pub fn deg2rad_(&mut self) -> Tensor
pub fn deg2rad_out(&self, out: &Tensor) -> Tensor
pub fn dense_dim(&self) -> i64
pub fn dequantize(&self) -> Tensor
pub fn det(&self) -> Tensor
pub fn detach(&self) -> Tensor
pub fn detach_(&mut self) -> Tensor
pub fn diag(&self, diagonal: i64) -> Tensor
pub fn diag_embed(&self, offset: i64, dim1: i64, dim2: i64) -> Tensor
pub fn diag_out(&self, out: &Tensor, diagonal: i64) -> Tensor
pub fn diagflat(&self, offset: i64) -> Tensor
pub fn diagonal(&self, offset: i64, dim1: i64, dim2: i64) -> Tensor
pub fn diagonal_scatter(
&self,
src: &Tensor,
offset: i64,
dim1: i64,
dim2: i64
) -> Tensor
pub fn diff<T>(
&self,
n: i64,
dim: i64,
prepend: Option<T>,
append: Option<T>
) -> Tensor where
T: Borrow<Tensor>,
pub fn diff_out<T>(
&self,
out: &Tensor,
n: i64,
dim: i64,
prepend: Option<T>,
append: Option<T>
) -> Tensor where
T: Borrow<Tensor>,
pub fn digamma(&self) -> Tensor
pub fn digamma_(&mut self) -> Tensor
pub fn digamma_out(&self, out: &Tensor) -> Tensor
pub fn dist(&self, other: &Tensor) -> Tensor
pub fn g_div(&self, other: &Tensor) -> Tensor
pub fn g_div_(&mut self, other: &Tensor) -> Tensor
pub fn div_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn div_out_mode(
&self,
out: &Tensor,
other: &Tensor,
rounding_mode: &str
) -> Tensor
pub fn g_div_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn g_div_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn g_div_scalar_mode<S>(&self, other: S, rounding_mode: &str) -> Tensor where
S: Into<Scalar>,
pub fn g_div_scalar_mode_<S>(&mut self, other: S, rounding_mode: &str) -> Tensor where
S: Into<Scalar>,
pub fn g_div_tensor_mode(&self, other: &Tensor, rounding_mode: &str) -> Tensor
pub fn g_div_tensor_mode_(
&mut self,
other: &Tensor,
rounding_mode: &str
) -> Tensor
pub fn divide(&self, other: &Tensor) -> Tensor
pub fn divide_(&mut self, other: &Tensor) -> Tensor
pub fn divide_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn divide_out_mode(
&self,
out: &Tensor,
other: &Tensor,
rounding_mode: &str
) -> Tensor
pub fn divide_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn divide_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn divide_scalar_mode<S>(&self, other: S, rounding_mode: &str) -> Tensor where
S: Into<Scalar>,
pub fn divide_scalar_mode_<S>(
&mut self,
other: S,
rounding_mode: &str
) -> Tensor where
S: Into<Scalar>,
pub fn divide_tensor_mode(&self, other: &Tensor, rounding_mode: &str) -> Tensor
pub fn divide_tensor_mode_(
&mut self,
other: &Tensor,
rounding_mode: &str
) -> Tensor
pub fn dot(&self, tensor: &Tensor) -> Tensor
pub fn dot_out(&self, out: &Tensor, tensor: &Tensor) -> Tensor
pub fn dropout(&self, p: f64, train: bool) -> Tensor
pub fn dropout_(&mut self, p: f64, train: bool) -> Tensor
pub fn dsplit(&self, sections: i64) -> Vec<Tensor, Global>
pub fn dsplit_array(&self, indices: &[i64]) -> Vec<Tensor, Global>
pub fn eig(&self, eigenvectors: bool) -> (Tensor, Tensor)
pub fn eig_e(
&self,
e: &Tensor,
v: &Tensor,
eigenvectors: bool
) -> (Tensor, Tensor)
pub fn elu(&self) -> Tensor
pub fn elu_(&mut self) -> Tensor
pub fn elu_out(&self, out: &Tensor) -> Tensor
pub fn embedding_renorm_(
&mut self,
indices: &Tensor,
max_norm: f64,
norm_type: f64
) -> Tensor
pub fn empty_like(&self) -> Tensor
pub fn eq<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn eq_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn eq_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn eq_tensor(&self, other: &Tensor) -> Tensor
pub fn eq_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn eq_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn equal(&self, other: &Tensor) -> bool
pub fn erf(&self) -> Tensor
pub fn erf_(&mut self) -> Tensor
pub fn erf_out(&self, out: &Tensor) -> Tensor
pub fn erfc(&self) -> Tensor
pub fn erfc_(&mut self) -> Tensor
pub fn erfc_out(&self, out: &Tensor) -> Tensor
pub fn erfinv(&self) -> Tensor
pub fn erfinv_(&mut self) -> Tensor
pub fn erfinv_out(&self, out: &Tensor) -> Tensor
pub fn exp(&self) -> Tensor
pub fn exp2(&self) -> Tensor
pub fn exp2_(&mut self) -> Tensor
pub fn exp2_out(&self, out: &Tensor) -> Tensor
pub fn exp_(&mut self) -> Tensor
pub fn exp_out(&self, out: &Tensor) -> Tensor
pub fn expand(&self, size: &[i64], implicit: bool) -> Tensor
pub fn expand_as(&self, other: &Tensor) -> Tensor
pub fn expm1(&self) -> Tensor
pub fn expm1_(&mut self) -> Tensor
pub fn expm1_out(&self, out: &Tensor) -> Tensor
pub fn exponential_(&mut self, lambd: f64) -> Tensor
pub fn fake_quantize_per_channel_affine(
&self,
scale: &Tensor,
zero_point: &Tensor,
axis: i64,
quant_min: i64,
quant_max: i64
) -> Tensor
pub fn fake_quantize_per_channel_affine_cachemask(
&self,
scale: &Tensor,
zero_point: &Tensor,
axis: i64,
quant_min: i64,
quant_max: i64
) -> (Tensor, Tensor)
pub fn fake_quantize_per_tensor_affine(
&self,
scale: f64,
zero_point: i64,
quant_min: i64,
quant_max: i64
) -> Tensor
pub fn fake_quantize_per_tensor_affine_cachemask(
&self,
scale: f64,
zero_point: i64,
quant_min: i64,
quant_max: i64
) -> (Tensor, Tensor)
pub fn fake_quantize_per_tensor_affine_tensor_qparams(
&self,
scale: &Tensor,
zero_point: &Tensor,
quant_min: i64,
quant_max: i64
) -> Tensor
pub fn fbgemm_linear_fp16_weight(
&self,
packed_weight: &Tensor,
bias: &Tensor
) -> Tensor
pub fn fbgemm_linear_fp16_weight_fp32_activation(
&self,
packed_weight: &Tensor,
bias: &Tensor
) -> Tensor
pub fn fbgemm_linear_int8_weight<S>(
&self,
weight: &Tensor,
packed: &Tensor,
col_offsets: &Tensor,
weight_scale: S,
weight_zero_point: S,
bias: &Tensor
) -> Tensor where
S: Into<Scalar>,
pub fn fbgemm_linear_int8_weight_fp32_activation<S>(
&self,
weight: &Tensor,
packed: &Tensor,
col_offsets: &Tensor,
weight_scale: S,
weight_zero_point: S,
bias: &Tensor
) -> Tensor where
S: Into<Scalar>,
pub fn fbgemm_pack_gemm_matrix_fp16(&self) -> Tensor
pub fn fbgemm_pack_quantized_matrix(&self) -> Tensor
pub fn fbgemm_pack_quantized_matrix_kn(&self, k: i64, n: i64) -> Tensor
pub fn feature_alpha_dropout(&self, p: f64, train: bool) -> Tensor
pub fn feature_alpha_dropout_(&mut self, p: f64, train: bool) -> Tensor
pub fn feature_dropout(&self, p: f64, train: bool) -> Tensor
pub fn feature_dropout_(&mut self, p: f64, train: bool) -> Tensor
pub fn fft_fft(&self, n: impl Into<Option<i64>>, dim: i64, norm: &str) -> Tensor
pub fn fft_fft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_fft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_fft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Tensor
pub fn fft_fftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fft_fftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fft_fftshift<'a>(&self, dim: impl Into<Option<&'a [i64]>>) -> Tensor
pub fn fft_hfft(
&self,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Tensor
pub fn fft_hfft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_hfft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_hfft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Tensor
pub fn fft_hfftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fft_hfftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fft_ifft(
&self,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Tensor
pub fn fft_ifft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_ifft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_ifft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Tensor
pub fn fft_ifftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fft_ifftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fft_ifftshift<'a>(&self, dim: impl Into<Option<&'a [i64]>>) -> Tensor
pub fn fft_ihfft(
&self,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Tensor
pub fn fft_ihfft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_ihfft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_ihfft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Tensor
pub fn fft_ihfftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fft_ihfftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fft_irfft(
&self,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Tensor
pub fn fft_irfft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_irfft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_irfft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Tensor
pub fn fft_irfftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fft_irfftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fft_rfft(
&self,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Tensor
pub fn fft_rfft2<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_rfft2_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: &[i64],
norm: &str
) -> Tensor
pub fn fft_rfft_out(
&self,
out: &Tensor,
n: impl Into<Option<i64>>,
dim: i64,
norm: &str
) -> Tensor
pub fn fft_rfftn<'a>(
&self,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fft_rfftn_out<'a>(
&self,
out: &Tensor,
s: impl Into<Option<&'a [i64]>>,
dim: impl Into<Option<&'a [i64]>>,
norm: &str
) -> Tensor
pub fn fill_<S>(&mut self, value: S) -> Tensor where
S: Into<Scalar>,
pub fn fill_diagonal_<S>(&mut self, fill_value: S, wrap: bool) -> Tensor where
S: Into<Scalar>,
pub fn fill_tensor_(&mut self, value: &Tensor) -> Tensor
pub fn fix(&self) -> Tensor
pub fn fix_(&mut self) -> Tensor
pub fn fix_out(&self, out: &Tensor) -> Tensor
pub fn flatten(&self, start_dim: i64, end_dim: i64) -> Tensor
pub fn flip(&self, dims: &[i64]) -> Tensor
pub fn fliplr(&self) -> Tensor
pub fn flipud(&self) -> Tensor
pub fn float_power(&self, exponent: &Tensor) -> Tensor
pub fn float_power_<S>(&mut self, exponent: S) -> Tensor where
S: Into<Scalar>,
pub fn float_power_tensor_(&mut self, exponent: &Tensor) -> Tensor
pub fn float_power_tensor_scalar<S>(&self, exponent: S) -> Tensor where
S: Into<Scalar>,
pub fn float_power_tensor_scalar_out<S>(
&self,
out: &Tensor,
exponent: S
) -> Tensor where
S: Into<Scalar>,
pub fn float_power_tensor_tensor_out(
&self,
out: &Tensor,
exponent: &Tensor
) -> Tensor
pub fn floor(&self) -> Tensor
pub fn floor_(&mut self) -> Tensor
pub fn floor_divide(&self, other: &Tensor) -> Tensor
pub fn floor_divide_(&mut self, other: &Tensor) -> Tensor
pub fn floor_divide_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn floor_divide_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn floor_divide_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn floor_out(&self, out: &Tensor) -> Tensor
pub fn fmax(&self, other: &Tensor) -> Tensor
pub fn fmax_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn fmin(&self, other: &Tensor) -> Tensor
pub fn fmin_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn fmod<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn fmod_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn fmod_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn fmod_tensor(&self, other: &Tensor) -> Tensor
pub fn fmod_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn fmod_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn frac(&self) -> Tensor
pub fn frac_(&mut self) -> Tensor
pub fn frac_out(&self, out: &Tensor) -> Tensor
pub fn fractional_max_pool2d(
&self,
kernel_size: &[i64],
output_size: &[i64],
random_samples: &Tensor
) -> (Tensor, Tensor)
pub fn fractional_max_pool2d_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
indices: &Tensor
) -> Tensor
pub fn fractional_max_pool2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
indices: &Tensor
) -> Tensor
pub fn fractional_max_pool2d_output(
&self,
output: &Tensor,
indices: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
random_samples: &Tensor
) -> (Tensor, Tensor)
pub fn fractional_max_pool3d(
&self,
kernel_size: &[i64],
output_size: &[i64],
random_samples: &Tensor
) -> (Tensor, Tensor)
pub fn fractional_max_pool3d_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
indices: &Tensor
) -> Tensor
pub fn fractional_max_pool3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
indices: &Tensor
) -> Tensor
pub fn fractional_max_pool3d_output(
&self,
output: &Tensor,
indices: &Tensor,
kernel_size: &[i64],
output_size: &[i64],
random_samples: &Tensor
) -> (Tensor, Tensor)
pub fn frexp(&self) -> (Tensor, Tensor)
pub fn frexp_tensor_out(
&self,
mantissa: &Tensor,
exponent: &Tensor
) -> (Tensor, Tensor)
pub fn frobenius_norm(&self) -> Tensor
pub fn frobenius_norm_dim(&self, dim: &[i64], keepdim: bool) -> Tensor
pub fn frobenius_norm_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool
) -> Tensor
pub fn full_like<S>(&self, fill_value: S) -> Tensor where
S: Into<Scalar>,
pub fn fused_moving_avg_obs_fake_quant(
&self,
observer_on: &Tensor,
fake_quant_on: &Tensor,
running_min: &Tensor,
running_max: &Tensor,
scale: &Tensor,
zero_point: &Tensor,
averaging_const: f64,
quant_min: i64,
quant_max: i64,
ch_axis: i64,
per_row_fake_quant: bool,
symmetric_quant: bool
) -> Tensor
pub fn gather(&self, dim: i64, index: &Tensor, sparse_grad: bool) -> Tensor
pub fn gather_backward(
&self,
grad: &Tensor,
dim: i64,
index: &Tensor,
sparse_grad: bool
) -> Tensor
pub fn gather_out(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
sparse_grad: bool
) -> Tensor
pub fn gcd(&self, other: &Tensor) -> Tensor
pub fn gcd_(&mut self, other: &Tensor) -> Tensor
pub fn gcd_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn ge<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn ge_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn ge_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn ge_tensor(&self, other: &Tensor) -> Tensor
pub fn ge_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn ge_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn gelu(&self) -> Tensor
pub fn gelu_backward(&self, grad: &Tensor) -> Tensor
pub fn gelu_backward_grad_input(
&self,
grad_input: &Tensor,
grad: &Tensor
) -> Tensor
pub fn gelu_out(&self, out: &Tensor) -> Tensor
pub fn geometric_(&mut self, p: f64) -> Tensor
pub fn geqrf(&self) -> (Tensor, Tensor)
pub fn geqrf_a(&self, a: &Tensor, tau: &Tensor) -> (Tensor, Tensor)
pub fn ger(&self, vec2: &Tensor) -> Tensor
pub fn ger_out(&self, out: &Tensor, vec2: &Tensor) -> Tensor
pub fn glu(&self, dim: i64) -> Tensor
pub fn glu_backward(&self, grad_output: &Tensor, dim: i64) -> Tensor
pub fn glu_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
dim: i64
) -> Tensor
pub fn glu_out(&self, out: &Tensor, dim: i64) -> Tensor
pub fn grad(&self) -> Tensor
pub fn greater<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn greater_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn greater_equal<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn greater_equal_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn greater_equal_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn greater_equal_tensor(&self, other: &Tensor) -> Tensor
pub fn greater_equal_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn greater_equal_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn greater_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn greater_tensor(&self, other: &Tensor) -> Tensor
pub fn greater_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn greater_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn grid_sampler(
&self,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> Tensor
pub fn grid_sampler_2d(
&self,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> Tensor
pub fn grid_sampler_3d(
&self,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> Tensor
pub fn grid_sampler_3d_backward(
&self,
grad_output: &Tensor,
grid: &Tensor,
interpolation_mode: i64,
padding_mode: i64,
align_corners: bool
) -> (Tensor, Tensor)
pub fn group_norm<T>(
&self,
num_groups: i64,
weight: Option<T>,
bias: Option<T>,
eps: f64,
cudnn_enabled: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn gru<T>(
&self,
hx: &Tensor,
params: &[T],
has_biases: bool,
num_layers: i64,
dropout: f64,
train: bool,
bidirectional: bool,
batch_first: bool
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn gru_cell<T>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: Option<T>,
b_hh: Option<T>
) -> Tensor where
T: Borrow<Tensor>,
pub fn gt<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn gt_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn gt_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn gt_tensor(&self, other: &Tensor) -> Tensor
pub fn gt_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn gt_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn hardshrink(&self) -> Tensor
pub fn hardshrink_backward<S>(&self, grad_out: &Tensor, lambd: S) -> Tensor where
S: Into<Scalar>,
pub fn hardshrink_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_out: &Tensor,
lambd: S
) -> Tensor where
S: Into<Scalar>,
pub fn hardshrink_out(&self, out: &Tensor) -> Tensor
pub fn hardsigmoid(&self) -> Tensor
pub fn hardsigmoid_(&mut self) -> Tensor
pub fn hardsigmoid_backward(&self, grad_output: &Tensor) -> Tensor
pub fn hardsigmoid_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor
) -> Tensor
pub fn hardsigmoid_out(&self, out: &Tensor) -> Tensor
pub fn hardswish(&self) -> Tensor
pub fn hardswish_(&mut self) -> Tensor
pub fn hardswish_backward(&self, grad_output: &Tensor) -> Tensor
pub fn hardswish_out(&self, out: &Tensor) -> Tensor
pub fn hardtanh(&self) -> Tensor
pub fn hardtanh_(&mut self) -> Tensor
pub fn hardtanh_backward<S>(
&self,
grad_output: &Tensor,
min_val: S,
max_val: S
) -> Tensor where
S: Into<Scalar>,
pub fn hardtanh_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
min_val: S,
max_val: S
) -> Tensor where
S: Into<Scalar>,
pub fn hardtanh_out(&self, out: &Tensor) -> Tensor
pub fn heaviside(&self, values: &Tensor) -> Tensor
pub fn heaviside_(&mut self, values: &Tensor) -> Tensor
pub fn heaviside_out(&self, out: &Tensor, values: &Tensor) -> Tensor
pub fn hinge_embedding_loss(
&self,
target: &Tensor,
margin: f64,
reduction: Reduction
) -> Tensor
pub fn histc(&self, bins: i64) -> Tensor
pub fn histc_out(&self, out: &Tensor, bins: i64) -> Tensor
pub fn histogram<T>(
&self,
bins: &Tensor,
weight: Option<T>,
density: bool
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn histogram_bin_ct<T>(
&self,
bins: i64,
range: &[f64],
weight: Option<T>,
density: bool
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn histogram_bin_ct_out<T>(
&self,
hist: &Tensor,
bin_edges: &Tensor,
bins: i64,
range: &[f64],
weight: Option<T>,
density: bool
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn histogram_bins_tensor_out<T>(
&self,
hist: &Tensor,
bin_edges: &Tensor,
bins: &Tensor,
weight: Option<T>,
density: bool
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn hsplit(&self, sections: i64) -> Vec<Tensor, Global>
pub fn hsplit_array(&self, indices: &[i64]) -> Vec<Tensor, Global>
pub fn huber_loss(
&self,
target: &Tensor,
reduction: Reduction,
delta: f64
) -> Tensor
pub fn huber_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
delta: f64
) -> Tensor
pub fn huber_loss_backward_out(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
delta: f64
) -> Tensor
pub fn huber_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction,
delta: f64
) -> Tensor
pub fn hypot(&self, other: &Tensor) -> Tensor
pub fn hypot_(&mut self, other: &Tensor) -> Tensor
pub fn hypot_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn i0(&self) -> Tensor
pub fn i0_(&mut self) -> Tensor
pub fn i0_out(&self, out: &Tensor) -> Tensor
pub fn igamma(&self, other: &Tensor) -> Tensor
pub fn igamma_(&mut self, other: &Tensor) -> Tensor
pub fn igamma_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn igammac(&self, other: &Tensor) -> Tensor
pub fn igammac_(&mut self, other: &Tensor) -> Tensor
pub fn igammac_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn im2col(
&self,
kernel_size: &[i64],
dilation: &[i64],
padding: &[i64],
stride: &[i64]
) -> Tensor
pub fn im2col_out(
&self,
out: &Tensor,
kernel_size: &[i64],
dilation: &[i64],
padding: &[i64],
stride: &[i64]
) -> Tensor
pub fn imag(&self) -> Tensor
pub fn index<T>(&self, indices: &[Option<T>]) -> Tensor where
T: Borrow<Tensor>,
pub fn index_add(&self, dim: i64, index: &Tensor, source: &Tensor) -> Tensor
pub fn index_add_(
&mut self,
dim: i64,
index: &Tensor,
source: &Tensor
) -> Tensor
pub fn index_add_out(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
source: &Tensor
) -> Tensor
pub fn index_copy(&self, dim: i64, index: &Tensor, source: &Tensor) -> Tensor
pub fn index_copy_(
&mut self,
dim: i64,
index: &Tensor,
source: &Tensor
) -> Tensor
pub fn index_fill<S>(&self, dim: i64, index: &Tensor, value: S) -> Tensor where
S: Into<Scalar>,
pub fn index_fill_<S>(&mut self, dim: i64, index: &Tensor, value: S) -> Tensor where
S: Into<Scalar>,
pub fn index_fill_int_tensor(
&self,
dim: i64,
index: &Tensor,
value: &Tensor
) -> Tensor
pub fn index_fill_int_tensor_(
&mut self,
dim: i64,
index: &Tensor,
value: &Tensor
) -> Tensor
pub fn index_put<T>(
&self,
indices: &[Option<T>],
values: &Tensor,
accumulate: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn index_put_<T>(
&mut self,
indices: &[Option<T>],
values: &Tensor,
accumulate: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn index_select(&self, dim: i64, index: &Tensor) -> Tensor
pub fn index_select_out(&self, out: &Tensor, dim: i64, index: &Tensor) -> Tensor
pub fn indices(&self) -> Tensor
pub fn infinitely_differentiable_gelu_backward(&self, grad: &Tensor) -> Tensor
pub fn inner(&self, other: &Tensor) -> Tensor
pub fn inner_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn instance_norm<T>(
&self,
weight: Option<T>,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
use_input_stats: bool,
momentum: f64,
eps: f64,
cudnn_enabled: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn int_repr(&self) -> Tensor
pub fn inverse(&self) -> Tensor
pub fn inverse_out(&self, out: &Tensor) -> Tensor
pub fn is_coalesced(&self) -> bool
pub fn is_complex(&self) -> bool
pub fn is_conj(&self) -> bool
pub fn is_distributed(&self) -> bool
pub fn is_floating_point(&self) -> bool
pub fn is_inference(&self) -> bool
pub fn is_leaf(&self) -> bool
pub fn is_neg(&self) -> bool
pub fn is_nonzero(&self) -> bool
pub fn is_pinned(&self, device: Device) -> bool
pub fn is_same_size(&self, other: &Tensor) -> bool
pub fn is_set_to(&self, tensor: &Tensor) -> bool
pub fn is_signed(&self) -> bool
pub fn isclose(
&self,
other: &Tensor,
rtol: f64,
atol: f64,
equal_nan: bool
) -> Tensor
pub fn isfinite(&self) -> Tensor
pub fn isinf(&self) -> Tensor
pub fn isnan(&self) -> Tensor
pub fn isneginf(&self) -> Tensor
pub fn isneginf_out(&self, out: &Tensor) -> Tensor
pub fn isposinf(&self) -> Tensor
pub fn isposinf_out(&self, out: &Tensor) -> Tensor
pub fn isreal(&self) -> Tensor
pub fn istft<T>(
&self,
n_fft: i64,
hop_length: impl Into<Option<i64>>,
win_length: impl Into<Option<i64>>,
window: Option<T>,
center: bool,
normalized: bool,
onesided: bool,
length: impl Into<Option<i64>>,
return_complex: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn kl_div(
&self,
target: &Tensor,
reduction: Reduction,
log_target: bool
) -> Tensor
pub fn kl_div_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
log_target: bool
) -> Tensor
pub fn kron(&self, other: &Tensor) -> Tensor
pub fn kron_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn kthvalue(&self, k: i64, dim: i64, keepdim: bool) -> (Tensor, Tensor)
pub fn kthvalue_values(
&self,
values: &Tensor,
indices: &Tensor,
k: i64,
dim: i64,
keepdim: bool
) -> (Tensor, Tensor)
pub fn l1_loss(&self, target: &Tensor, reduction: Reduction) -> Tensor
pub fn l1_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Tensor
pub fn l1_loss_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Tensor
pub fn l1_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Tensor
pub fn layer_norm<T>(
&self,
normalized_shape: &[i64],
weight: Option<T>,
bias: Option<T>,
eps: f64,
cudnn_enable: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn lcm(&self, other: &Tensor) -> Tensor
pub fn lcm_(&mut self, other: &Tensor) -> Tensor
pub fn lcm_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn ldexp(&self, other: &Tensor) -> Tensor
pub fn ldexp_(&mut self, other: &Tensor) -> Tensor
pub fn ldexp_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn le<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn le_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn le_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn le_tensor(&self, other: &Tensor) -> Tensor
pub fn le_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn le_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn leaky_relu(&self) -> Tensor
pub fn leaky_relu_(&mut self) -> Tensor
pub fn leaky_relu_backward<S>(
&self,
grad_output: &Tensor,
negative_slope: S,
self_is_result: bool
) -> Tensor where
S: Into<Scalar>,
pub fn leaky_relu_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
negative_slope: S,
self_is_result: bool
) -> Tensor where
S: Into<Scalar>,
pub fn leaky_relu_out(&self, out: &Tensor) -> Tensor
pub fn lerp<S>(&self, end: &Tensor, weight: S) -> Tensor where
S: Into<Scalar>,
pub fn lerp_<S>(&mut self, end: &Tensor, weight: S) -> Tensor where
S: Into<Scalar>,
pub fn lerp_scalar_out<S>(
&self,
out: &Tensor,
end: &Tensor,
weight: S
) -> Tensor where
S: Into<Scalar>,
pub fn lerp_tensor(&self, end: &Tensor, weight: &Tensor) -> Tensor
pub fn lerp_tensor_(&mut self, end: &Tensor, weight: &Tensor) -> Tensor
pub fn lerp_tensor_out(
&self,
out: &Tensor,
end: &Tensor,
weight: &Tensor
) -> Tensor
pub fn less<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn less_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn less_equal<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn less_equal_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn less_equal_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn less_equal_tensor(&self, other: &Tensor) -> Tensor
pub fn less_equal_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn less_equal_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn less_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn less_tensor(&self, other: &Tensor) -> Tensor
pub fn less_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn less_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn lgamma(&self) -> Tensor
pub fn lgamma_(&mut self) -> Tensor
pub fn lgamma_out(&self, out: &Tensor) -> Tensor
pub fn linalg_cholesky(&self, upper: bool) -> Tensor
pub fn linalg_cholesky_ex(
&self,
upper: bool,
check_errors: bool
) -> (Tensor, Tensor)
pub fn linalg_cholesky_ex_l(
&self,
l: &Tensor,
info: &Tensor,
upper: bool,
check_errors: bool
) -> (Tensor, Tensor)
pub fn linalg_cholesky_out(&self, out: &Tensor, upper: bool) -> Tensor
pub fn linalg_cond<S>(&self, p: S) -> Tensor where
S: Into<Scalar>,
pub fn linalg_cond_out<S>(&self, out: &Tensor, p: S) -> Tensor where
S: Into<Scalar>,
pub fn linalg_cond_p_str(&self, p: &str) -> Tensor
pub fn linalg_cond_p_str_out(&self, out: &Tensor, p: &str) -> Tensor
pub fn linalg_cross(&self, other: &Tensor, dim: i64) -> Tensor
pub fn linalg_cross_out(&self, out: &Tensor, other: &Tensor, dim: i64) -> Tensor
pub fn linalg_det(&self) -> Tensor
pub fn linalg_det_out(&self, out: &Tensor) -> Tensor
pub fn linalg_eig(&self) -> (Tensor, Tensor)
pub fn linalg_eig_out(
&self,
eigenvalues: &Tensor,
eigenvectors: &Tensor
) -> (Tensor, Tensor)
pub fn linalg_eigh(&self, uplo: &str) -> (Tensor, Tensor)
pub fn linalg_eigh_eigvals(
&self,
eigvals: &Tensor,
eigvecs: &Tensor,
uplo: &str
) -> (Tensor, Tensor)
pub fn linalg_eigvals(&self) -> Tensor
pub fn linalg_eigvals_out(&self, out: &Tensor) -> Tensor
pub fn linalg_eigvalsh(&self, uplo: &str) -> Tensor
pub fn linalg_eigvalsh_out(&self, out: &Tensor, uplo: &str) -> Tensor
pub fn linalg_householder_product(&self, tau: &Tensor) -> Tensor
pub fn linalg_householder_product_out(
&self,
out: &Tensor,
tau: &Tensor
) -> Tensor
pub fn linalg_inv(&self) -> Tensor
pub fn linalg_inv_ex(&self, check_errors: bool) -> (Tensor, Tensor)
pub fn linalg_inv_ex_inverse(
&self,
inverse: &Tensor,
info: &Tensor,
check_errors: bool
) -> (Tensor, Tensor)
pub fn linalg_inv_out(&self, out: &Tensor) -> Tensor
pub fn linalg_lstsq(
&self,
b: &Tensor,
rcond: impl Into<Option<f64>>,
driver: &str
) -> (Tensor, Tensor, Tensor, Tensor)
pub fn linalg_lstsq_out(
&self,
solution: &Tensor,
residuals: &Tensor,
rank: &Tensor,
singular_values: &Tensor,
b: &Tensor,
rcond: impl Into<Option<f64>>,
driver: &str
) -> (Tensor, Tensor, Tensor, Tensor)
pub fn linalg_matmul(&self, other: &Tensor) -> Tensor
pub fn linalg_matmul_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn linalg_matrix_exp(&self) -> Tensor
pub fn linalg_matrix_power(&self, n: i64) -> Tensor
pub fn linalg_matrix_power_out(&self, out: &Tensor, n: i64) -> Tensor
pub fn linalg_matrix_rank(&self, tol: f64, hermitian: bool) -> Tensor
pub fn linalg_matrix_rank_atol_rtol_float(
&self,
atol: impl Into<Option<f64>>,
rtol: impl Into<Option<f64>>,
hermitian: bool
) -> Tensor
pub fn linalg_matrix_rank_atol_rtol_float_out(
&self,
out: &Tensor,
atol: impl Into<Option<f64>>,
rtol: impl Into<Option<f64>>,
hermitian: bool
) -> Tensor
pub fn linalg_matrix_rank_atol_rtol_tensor<T>(
&self,
atol: Option<T>,
rtol: Option<T>,
hermitian: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn linalg_matrix_rank_atol_rtol_tensor_out<T>(
&self,
out: &Tensor,
atol: Option<T>,
rtol: Option<T>,
hermitian: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn linalg_matrix_rank_out(
&self,
out: &Tensor,
tol: f64,
hermitian: bool
) -> Tensor
pub fn linalg_matrix_rank_out_tol_tensor(
&self,
out: &Tensor,
tol: &Tensor,
hermitian: bool
) -> Tensor
pub fn linalg_matrix_rank_tol_tensor(
&self,
tol: &Tensor,
hermitian: bool
) -> Tensor
pub fn linalg_norm<'a, S>(
&self,
ord: S,
dim: impl Into<Option<&'a [i64]>>,
keepdim: bool,
dtype: Kind
) -> Tensor where
S: Into<Scalar>,
pub fn linalg_norm_ord_str<'a>(
&self,
ord: &str,
dim: impl Into<Option<&'a [i64]>>,
keepdim: bool,
dtype: Kind
) -> Tensor
pub fn linalg_norm_ord_str_out<'a>(
&self,
out: &Tensor,
ord: &str,
dim: impl Into<Option<&'a [i64]>>,
keepdim: bool,
dtype: Kind
) -> Tensor
pub fn linalg_norm_out<'a, S>(
&self,
out: &Tensor,
ord: S,
dim: impl Into<Option<&'a [i64]>>,
keepdim: bool,
dtype: Kind
) -> Tensor where
S: Into<Scalar>,
pub fn linalg_pinv(&self, rcond: f64, hermitian: bool) -> Tensor
pub fn linalg_pinv_atol_rtol_float(
&self,
atol: impl Into<Option<f64>>,
rtol: impl Into<Option<f64>>,
hermitian: bool
) -> Tensor
pub fn linalg_pinv_atol_rtol_float_out(
&self,
out: &Tensor,
atol: impl Into<Option<f64>>,
rtol: impl Into<Option<f64>>,
hermitian: bool
) -> Tensor
pub fn linalg_pinv_atol_rtol_tensor<T>(
&self,
atol: Option<T>,
rtol: Option<T>,
hermitian: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn linalg_pinv_atol_rtol_tensor_out<T>(
&self,
out: &Tensor,
atol: Option<T>,
rtol: Option<T>,
hermitian: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn linalg_pinv_out(
&self,
out: &Tensor,
rcond: f64,
hermitian: bool
) -> Tensor
pub fn linalg_pinv_out_rcond_tensor(
&self,
out: &Tensor,
rcond: &Tensor,
hermitian: bool
) -> Tensor
pub fn linalg_pinv_rcond_tensor(
&self,
rcond: &Tensor,
hermitian: bool
) -> Tensor
pub fn linalg_qr(&self, mode: &str) -> (Tensor, Tensor)
pub fn linalg_qr_out(
&self,
q: &Tensor,
r: &Tensor,
mode: &str
) -> (Tensor, Tensor)
pub fn linalg_slogdet(&self) -> (Tensor, Tensor)
pub fn linalg_slogdet_out(
&self,
sign: &Tensor,
logabsdet: &Tensor
) -> (Tensor, Tensor)
pub fn linalg_solve(&self, other: &Tensor) -> Tensor
pub fn linalg_solve_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn linalg_solve_triangular(
&self,
b: &Tensor,
upper: bool,
left: bool,
unitriangular: bool
) -> Tensor
pub fn linalg_solve_triangular_out(
&self,
out: &Tensor,
b: &Tensor,
upper: bool,
left: bool,
unitriangular: bool
) -> Tensor
pub fn linalg_tensorinv(&self, ind: i64) -> Tensor
pub fn linalg_tensorinv_out(&self, out: &Tensor, ind: i64) -> Tensor
pub fn linalg_tensorsolve<'a>(
&self,
other: &Tensor,
dims: impl Into<Option<&'a [i64]>>
) -> Tensor
pub fn linalg_tensorsolve_out<'a>(
&self,
out: &Tensor,
other: &Tensor,
dims: impl Into<Option<&'a [i64]>>
) -> Tensor
pub fn linear<T>(&self, weight: &Tensor, bias: Option<T>) -> Tensor where
T: Borrow<Tensor>,
pub fn linear_out<T>(
&self,
out: &Tensor,
weight: &Tensor,
bias: Option<T>
) -> Tensor where
T: Borrow<Tensor>,
pub fn log(&self) -> Tensor
pub fn log10(&self) -> Tensor
pub fn log10_(&mut self) -> Tensor
pub fn log10_out(&self, out: &Tensor) -> Tensor
pub fn log1p(&self) -> Tensor
pub fn log1p_(&mut self) -> Tensor
pub fn log1p_out(&self, out: &Tensor) -> Tensor
pub fn log2(&self) -> Tensor
pub fn log2_(&mut self) -> Tensor
pub fn log2_out(&self, out: &Tensor) -> Tensor
pub fn log_(&mut self) -> Tensor
pub fn log_normal_(&mut self, mean: f64, std: f64) -> Tensor
pub fn log_out(&self, out: &Tensor) -> Tensor
pub fn log_sigmoid(&self) -> Tensor
pub fn log_sigmoid_backward(
&self,
grad_output: &Tensor,
buffer: &Tensor
) -> Tensor
pub fn log_sigmoid_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
buffer: &Tensor
) -> Tensor
pub fn log_sigmoid_out(&self, out: &Tensor) -> Tensor
pub fn log_softmax(&self, dim: i64, dtype: Kind) -> Tensor
pub fn logaddexp(&self, other: &Tensor) -> Tensor
pub fn logaddexp2(&self, other: &Tensor) -> Tensor
pub fn logaddexp2_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn logaddexp_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn logcumsumexp(&self, dim: i64) -> Tensor
pub fn logcumsumexp_out(&self, out: &Tensor, dim: i64) -> Tensor
pub fn logdet(&self) -> Tensor
pub fn logical_and(&self, other: &Tensor) -> Tensor
pub fn logical_and_(&mut self, other: &Tensor) -> Tensor
pub fn logical_and_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn logical_not(&self) -> Tensor
pub fn logical_not_(&mut self) -> Tensor
pub fn logical_not_out(&self, out: &Tensor) -> Tensor
pub fn logical_or(&self, other: &Tensor) -> Tensor
pub fn logical_or_(&mut self, other: &Tensor) -> Tensor
pub fn logical_or_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn logical_xor(&self, other: &Tensor) -> Tensor
pub fn logical_xor_(&mut self, other: &Tensor) -> Tensor
pub fn logical_xor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn logit(&self, eps: impl Into<Option<f64>>) -> Tensor
pub fn logit_(&mut self, eps: impl Into<Option<f64>>) -> Tensor
pub fn logit_backward(
&self,
grad_output: &Tensor,
eps: impl Into<Option<f64>>
) -> Tensor
pub fn logit_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
eps: impl Into<Option<f64>>
) -> Tensor
pub fn logit_out(&self, out: &Tensor, eps: impl Into<Option<f64>>) -> Tensor
pub fn logsumexp(&self, dim: &[i64], keepdim: bool) -> Tensor
pub fn logsumexp_out(&self, out: &Tensor, dim: &[i64], keepdim: bool) -> Tensor
pub fn lstm<T>(
&self,
hx: &[T],
params: &[T],
has_biases: bool,
num_layers: i64,
dropout: f64,
train: bool,
bidirectional: bool,
batch_first: bool
) -> (Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn lstm_cell<T>(
&self,
hx: &[T],
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: Option<T>,
b_hh: Option<T>
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn lstsq(&self, a: &Tensor) -> (Tensor, Tensor)
pub fn lstsq_x(&self, x: &Tensor, qr: &Tensor, a: &Tensor) -> (Tensor, Tensor)
pub fn lt<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn lt_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn lt_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn lt_tensor(&self, other: &Tensor) -> Tensor
pub fn lt_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn lt_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn lu_solve(&self, lu_data: &Tensor, lu_pivots: &Tensor) -> Tensor
pub fn lu_solve_out(
&self,
out: &Tensor,
lu_data: &Tensor,
lu_pivots: &Tensor
) -> Tensor
pub fn masked_fill<S>(&self, mask: &Tensor, value: S) -> Tensor where
S: Into<Scalar>,
pub fn masked_fill_<S>(&mut self, mask: &Tensor, value: S) -> Tensor where
S: Into<Scalar>,
pub fn masked_fill_tensor(&self, mask: &Tensor, value: &Tensor) -> Tensor
pub fn masked_fill_tensor_(&mut self, mask: &Tensor, value: &Tensor) -> Tensor
pub fn masked_scatter(&self, mask: &Tensor, source: &Tensor) -> Tensor
pub fn masked_scatter_(&mut self, mask: &Tensor, source: &Tensor) -> Tensor
pub fn masked_select(&self, mask: &Tensor) -> Tensor
pub fn masked_select_backward(&self, grad: &Tensor, mask: &Tensor) -> Tensor
pub fn masked_select_out(&self, out: &Tensor, mask: &Tensor) -> Tensor
pub fn matmul(&self, other: &Tensor) -> Tensor
pub fn matmul_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn matrix_exp(&self) -> Tensor
pub fn matrix_exp_backward(&self, grad: &Tensor) -> Tensor
pub fn matrix_h(&self) -> Tensor
pub fn matrix_power(&self, n: i64) -> Tensor
pub fn matrix_power_out(&self, out: &Tensor, n: i64) -> Tensor
pub fn matrix_rank(&self, symmetric: bool) -> Tensor
pub fn matrix_rank_tol(&self, tol: f64, symmetric: bool) -> Tensor
pub fn max(&self) -> Tensor
pub fn max_dim(&self, dim: i64, keepdim: bool) -> (Tensor, Tensor)
pub fn max_dim_max(
&self,
max: &Tensor,
max_values: &Tensor,
dim: i64,
keepdim: bool
) -> (Tensor, Tensor)
pub fn max_other(&self, other: &Tensor) -> Tensor
pub fn max_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn max_pool1d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Tensor
pub fn max_pool1d_with_indices(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> (Tensor, Tensor)
pub fn max_pool2d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Tensor
pub fn max_pool2d_with_indices(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> (Tensor, Tensor)
pub fn max_pool2d_with_indices_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool,
indices: &Tensor
) -> Tensor
pub fn max_pool2d_with_indices_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool,
indices: &Tensor
) -> Tensor
pub fn max_pool2d_with_indices_out(
&self,
out: &Tensor,
indices: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> (Tensor, Tensor)
pub fn max_pool3d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Tensor
pub fn max_pool3d_with_indices(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> (Tensor, Tensor)
pub fn max_pool3d_with_indices_backward(
&self,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool,
indices: &Tensor
) -> Tensor
pub fn max_pool3d_with_indices_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool,
indices: &Tensor
) -> Tensor
pub fn max_pool3d_with_indices_out(
&self,
out: &Tensor,
indices: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> (Tensor, Tensor)
pub fn max_unpool2d(&self, indices: &Tensor, output_size: &[i64]) -> Tensor
pub fn max_unpool2d_backward(
&self,
grad_output: &Tensor,
indices: &Tensor,
output_size: &[i64]
) -> Tensor
pub fn max_unpool2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
indices: &Tensor,
output_size: &[i64]
) -> Tensor
pub fn max_unpool2d_out(
&self,
out: &Tensor,
indices: &Tensor,
output_size: &[i64]
) -> Tensor
pub fn max_unpool3d(
&self,
indices: &Tensor,
output_size: &[i64],
stride: &[i64],
padding: &[i64]
) -> Tensor
pub fn max_unpool3d_backward(
&self,
grad_output: &Tensor,
indices: &Tensor,
output_size: &[i64],
stride: &[i64],
padding: &[i64]
) -> Tensor
pub fn max_unpool3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
indices: &Tensor,
output_size: &[i64],
stride: &[i64],
padding: &[i64]
) -> Tensor
pub fn max_unpool3d_out(
&self,
out: &Tensor,
indices: &Tensor,
output_size: &[i64],
stride: &[i64],
padding: &[i64]
) -> Tensor
pub fn maximum(&self, other: &Tensor) -> Tensor
pub fn maximum_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn mean(&self, dtype: Kind) -> Tensor
pub fn mean_dim(&self, dim: &[i64], keepdim: bool, dtype: Kind) -> Tensor
pub fn mean_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Tensor
pub fn median(&self) -> Tensor
pub fn median_dim(&self, dim: i64, keepdim: bool) -> (Tensor, Tensor)
pub fn median_dim_values(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64,
keepdim: bool
) -> (Tensor, Tensor)
pub fn mh(&self) -> Tensor
pub fn min(&self) -> Tensor
pub fn min_dim(&self, dim: i64, keepdim: bool) -> (Tensor, Tensor)
pub fn min_dim_min(
&self,
min: &Tensor,
min_indices: &Tensor,
dim: i64,
keepdim: bool
) -> (Tensor, Tensor)
pub fn min_other(&self, other: &Tensor) -> Tensor
pub fn min_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn minimum(&self, other: &Tensor) -> Tensor
pub fn minimum_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn miopen_batch_norm<T>(
&self,
weight: &Tensor,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
training: bool,
exponential_average_factor: f64,
epsilon: f64
) -> (Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn miopen_batch_norm_backward<T>(
&self,
grad_output: &Tensor,
weight: &Tensor,
running_mean: Option<T>,
running_var: Option<T>,
save_mean: Option<T>,
save_var: Option<T>,
epsilon: f64
) -> (Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn miopen_convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn miopen_convolution_transpose<T>(
&self,
weight: &Tensor,
bias: Option<T>,
padding: &[i64],
output_padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn miopen_depthwise_convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64,
benchmark: bool,
deterministic: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn miopen_rnn<T>(
&self,
weight: &[T],
weight_stride0: i64,
hx: &Tensor,
cx: Option<T>,
mode: i64,
hidden_size: i64,
num_layers: i64,
batch_first: bool,
dropout: f64,
train: bool,
bidirectional: bool,
batch_sizes: &[i64],
dropout_state: Option<T>
) -> (Tensor, Tensor, Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn mish(&self) -> Tensor
pub fn mish_(&mut self) -> Tensor
pub fn mish_backward(&self, grad_output: &Tensor) -> Tensor
pub fn mish_out(&self, out: &Tensor) -> Tensor
pub fn mkldnn_adaptive_avg_pool2d(&self, output_size: &[i64]) -> Tensor
pub fn mkldnn_adaptive_avg_pool2d_backward(
&self,
grad_output: &Tensor
) -> Tensor
pub fn mkldnn_convolution<T>(
&self,
weight: &Tensor,
bias: Option<T>,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn mkldnn_linear<T>(&self, weight: &Tensor, bias: Option<T>) -> Tensor where
T: Borrow<Tensor>,
pub fn mkldnn_linear_backward_weights(
&self,
grad_output: &Tensor,
weight: &Tensor,
bias_defined: bool
) -> (Tensor, Tensor)
pub fn mkldnn_max_pool2d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Tensor
pub fn mkldnn_max_pool2d_backward(
&self,
grad_output: &Tensor,
output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Tensor
pub fn mkldnn_max_pool3d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Tensor
pub fn mkldnn_max_pool3d_backward(
&self,
grad_output: &Tensor,
output: &Tensor,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Tensor
pub fn mkldnn_reorder_conv2d_weight(
&self,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64
) -> Tensor
pub fn mkldnn_reorder_conv3d_weight(
&self,
padding: &[i64],
stride: &[i64],
dilation: &[i64],
groups: i64
) -> Tensor
pub fn mm(&self, mat2: &Tensor) -> Tensor
pub fn mm_out(&self, out: &Tensor, mat2: &Tensor) -> Tensor
pub fn mode(&self, dim: i64, keepdim: bool) -> (Tensor, Tensor)
pub fn mode_values(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64,
keepdim: bool
) -> (Tensor, Tensor)
pub fn moveaxis(&self, source: &[i64], destination: &[i64]) -> Tensor
pub fn moveaxis_int(&self, source: i64, destination: i64) -> Tensor
pub fn movedim(&self, source: &[i64], destination: &[i64]) -> Tensor
pub fn movedim_int(&self, source: i64, destination: i64) -> Tensor
pub fn mse_loss(&self, target: &Tensor, reduction: Reduction) -> Tensor
pub fn mse_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Tensor
pub fn mse_loss_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Tensor
pub fn mse_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Tensor
pub fn msort(&self) -> Tensor
pub fn msort_out(&self, out: &Tensor) -> Tensor
pub fn mt(&self) -> Tensor
pub fn g_mul(&self, other: &Tensor) -> Tensor
pub fn g_mul_(&mut self, other: &Tensor) -> Tensor
pub fn mul_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn g_mul_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn g_mul_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn multi_margin_loss_backward<T, S>(
&self,
grad_output: &Tensor,
target: &Tensor,
p: S,
margin: S,
weight: Option<T>,
reduction: Reduction
) -> Tensor where
T: Borrow<Tensor>,
S: Into<Scalar>,
pub fn multi_margin_loss_backward_grad_input<T, S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
p: S,
margin: S,
weight: Option<T>,
reduction: Reduction
) -> Tensor where
T: Borrow<Tensor>,
S: Into<Scalar>,
pub fn multilabel_margin_loss(
&self,
target: &Tensor,
reduction: Reduction
) -> Tensor
pub fn multilabel_margin_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
is_target: &Tensor
) -> Tensor
pub fn multilabel_margin_loss_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
is_target: &Tensor
) -> Tensor
pub fn multilabel_margin_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Tensor
pub fn multinomial(&self, num_samples: i64, replacement: bool) -> Tensor
pub fn multinomial_out(
&self,
out: &Tensor,
num_samples: i64,
replacement: bool
) -> Tensor
pub fn multiply(&self, other: &Tensor) -> Tensor
pub fn multiply_(&mut self, other: &Tensor) -> Tensor
pub fn multiply_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn multiply_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn multiply_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn mv(&self, vec: &Tensor) -> Tensor
pub fn mv_out(&self, out: &Tensor, vec: &Tensor) -> Tensor
pub fn mvlgamma(&self, p: i64) -> Tensor
pub fn mvlgamma_(&mut self, p: i64) -> Tensor
pub fn mvlgamma_out(&self, out: &Tensor, p: i64) -> Tensor
pub fn nan_to_num(
&self,
nan: impl Into<Option<f64>>,
posinf: impl Into<Option<f64>>,
neginf: impl Into<Option<f64>>
) -> Tensor
pub fn nan_to_num_(
&mut self,
nan: impl Into<Option<f64>>,
posinf: impl Into<Option<f64>>,
neginf: impl Into<Option<f64>>
) -> Tensor
pub fn nan_to_num_out(
&self,
out: &Tensor,
nan: impl Into<Option<f64>>,
posinf: impl Into<Option<f64>>,
neginf: impl Into<Option<f64>>
) -> Tensor
pub fn nanmean(&self, dim: &[i64], keepdim: bool, dtype: Kind) -> Tensor
pub fn nanmean_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Tensor
pub fn nanmedian(&self) -> Tensor
pub fn nanmedian_dim(&self, dim: i64, keepdim: bool) -> (Tensor, Tensor)
pub fn nanmedian_dim_values(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64,
keepdim: bool
) -> (Tensor, Tensor)
pub fn nanquantile(
&self,
q: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Tensor
pub fn nanquantile_out(
&self,
out: &Tensor,
q: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Tensor
pub fn nanquantile_scalar(
&self,
q: f64,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Tensor
pub fn nanquantile_scalar_out(
&self,
out: &Tensor,
q: f64,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Tensor
pub fn nansum(&self, dtype: Kind) -> Tensor
pub fn nansum_dim_intlist(
&self,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Tensor
pub fn nansum_intlist_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Tensor
pub fn narrow(&self, dim: i64, start: i64, length: i64) -> Tensor
pub fn narrow_copy(&self, dim: i64, start: i64, length: i64) -> Tensor
pub fn narrow_copy_out(
&self,
out: &Tensor,
dim: i64,
start: i64,
length: i64
) -> Tensor
pub fn narrow_tensor(&self, dim: i64, start: &Tensor, length: i64) -> Tensor
pub fn native_batch_norm<T>(
&self,
weight: Option<T>,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
training: bool,
momentum: f64,
eps: f64
) -> (Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn native_batch_norm_out<T>(
&self,
out: &Tensor,
save_mean: &Tensor,
save_invstd: &Tensor,
weight: Option<T>,
bias: Option<T>,
running_mean: Option<T>,
running_var: Option<T>,
training: bool,
momentum: f64,
eps: f64
) -> (Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn native_channel_shuffle(&self, groups: i64) -> Tensor
pub fn native_dropout(&self, p: f64, train: bool) -> (Tensor, Tensor)
pub fn native_group_norm<T>(
&self,
weight: Option<T>,
bias: Option<T>,
n: i64,
c: i64,
hxw: i64,
group: i64,
eps: f64
) -> (Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn native_layer_norm<T>(
&self,
normalized_shape: &[i64],
weight: Option<T>,
bias: Option<T>,
eps: f64
) -> (Tensor, Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn native_norm(&self) -> Tensor
pub fn native_norm_scalaropt_dim_dtype<S>(
&self,
p: S,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Tensor where
S: Into<Scalar>,
pub fn ne<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn ne_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn ne_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn ne_tensor(&self, other: &Tensor) -> Tensor
pub fn ne_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn ne_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn neg(&self) -> Tensor
pub fn neg_(&mut self) -> Tensor
pub fn neg_out(&self, out: &Tensor) -> Tensor
pub fn negative(&self) -> Tensor
pub fn negative_(&mut self) -> Tensor
pub fn negative_out(&self, out: &Tensor) -> Tensor
pub fn new_empty(&self, size: &[i64], options: (Kind, Device)) -> Tensor
pub fn new_empty_strided(
&self,
size: &[i64],
stride: &[i64],
options: (Kind, Device)
) -> Tensor
pub fn new_full<S>(
&self,
size: &[i64],
fill_value: S,
options: (Kind, Device)
) -> Tensor where
S: Into<Scalar>,
pub fn new_ones(&self, size: &[i64], options: (Kind, Device)) -> Tensor
pub fn new_zeros(&self, size: &[i64], options: (Kind, Device)) -> Tensor
pub fn nextafter(&self, other: &Tensor) -> Tensor
pub fn nextafter_(&mut self, other: &Tensor) -> Tensor
pub fn nextafter_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn g_nll_loss<T>(
&self,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn nll_loss2d<T>(
&self,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn nll_loss2d_backward<T>(
&self,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64,
total_weight: &Tensor
) -> Tensor where
T: Borrow<Tensor>,
pub fn nll_loss2d_backward_grad_input<T>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64,
total_weight: &Tensor
) -> Tensor where
T: Borrow<Tensor>,
pub fn nll_loss2d_out<T>(
&self,
out: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn nll_loss_backward<T>(
&self,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64,
total_weight: &Tensor
) -> Tensor where
T: Borrow<Tensor>,
pub fn nll_loss_backward_grad_input<T>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64,
total_weight: &Tensor
) -> Tensor where
T: Borrow<Tensor>,
pub fn nll_loss_nd<T>(
&self,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn nll_loss_out<T>(
&self,
out: &Tensor,
target: &Tensor,
weight: Option<T>,
reduction: Reduction,
ignore_index: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn nonzero(&self) -> Tensor
pub fn nonzero_numpy(&self) -> Vec<Tensor, Global>
pub fn nonzero_out(&self, out: &Tensor) -> Tensor
pub fn norm(&self) -> Tensor
pub fn norm_dtype_out<S>(
&self,
out: &Tensor,
p: S,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Tensor where
S: Into<Scalar>,
pub fn norm_out<S>(
&self,
out: &Tensor,
p: S,
dim: &[i64],
keepdim: bool
) -> Tensor where
S: Into<Scalar>,
pub fn norm_scalaropt_dim<S>(&self, p: S, dim: &[i64], keepdim: bool) -> Tensor where
S: Into<Scalar>,
pub fn norm_scalaropt_dim_dtype<S>(
&self,
p: S,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Tensor where
S: Into<Scalar>,
pub fn norm_scalaropt_dtype<S>(&self, p: S, dtype: Kind) -> Tensor where
S: Into<Scalar>,
pub fn normal_(&mut self, mean: f64, std: f64) -> Tensor
pub fn not_equal<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn not_equal_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn not_equal_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn not_equal_tensor(&self, other: &Tensor) -> Tensor
pub fn not_equal_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn not_equal_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn nuclear_norm(&self, keepdim: bool) -> Tensor
pub fn nuclear_norm_dim(&self, dim: &[i64], keepdim: bool) -> Tensor
pub fn nuclear_norm_dim_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool
) -> Tensor
pub fn nuclear_norm_out(&self, out: &Tensor, keepdim: bool) -> Tensor
pub fn numpy_t(&self) -> Tensor
pub fn one_hot(&self, num_classes: i64) -> Tensor
pub fn ones_like(&self) -> Tensor
pub fn orgqr(&self, input2: &Tensor) -> Tensor
pub fn orgqr_out(&self, out: &Tensor, input2: &Tensor) -> Tensor
pub fn ormqr(
&self,
input2: &Tensor,
input3: &Tensor,
left: bool,
transpose: bool
) -> Tensor
pub fn ormqr_out(
&self,
out: &Tensor,
input2: &Tensor,
input3: &Tensor,
left: bool,
transpose: bool
) -> Tensor
pub fn outer(&self, vec2: &Tensor) -> Tensor
pub fn outer_out(&self, out: &Tensor, vec2: &Tensor) -> Tensor
pub fn output_nr(&self) -> i64
pub fn pdist(&self, p: f64) -> Tensor
pub fn permute(&self, dims: &[i64]) -> Tensor
pub fn pin_memory(&self, device: Device) -> Tensor
pub fn pinverse(&self, rcond: f64) -> Tensor
pub fn pixel_shuffle(&self, upscale_factor: i64) -> Tensor
pub fn pixel_unshuffle(&self, downscale_factor: i64) -> Tensor
pub fn poisson(&self) -> Tensor
pub fn poisson_nll_loss(
&self,
target: &Tensor,
log_input: bool,
full: bool,
eps: f64,
reduction: Reduction
) -> Tensor
pub fn polygamma(&self, n: i64) -> Tensor
pub fn polygamma_(&mut self, n: i64) -> Tensor
pub fn polygamma_out(&self, out: &Tensor, n: i64) -> Tensor
pub fn positive(&self) -> Tensor
pub fn pow(&self, exponent: &Tensor) -> Tensor
pub fn pow_<S>(&mut self, exponent: S) -> Tensor where
S: Into<Scalar>,
pub fn pow_tensor_(&mut self, exponent: &Tensor) -> Tensor
pub fn pow_tensor_scalar<S>(&self, exponent: S) -> Tensor where
S: Into<Scalar>,
pub fn pow_tensor_scalar_out<S>(&self, out: &Tensor, exponent: S) -> Tensor where
S: Into<Scalar>,
pub fn pow_tensor_tensor_out(&self, out: &Tensor, exponent: &Tensor) -> Tensor
pub fn prelu(&self, weight: &Tensor) -> Tensor
pub fn prelu_backward(
&self,
grad_output: &Tensor,
weight: &Tensor
) -> (Tensor, Tensor)
pub fn prod(&self, dtype: Kind) -> Tensor
pub fn prod_dim_int(&self, dim: i64, keepdim: bool, dtype: Kind) -> Tensor
pub fn prod_int_out(
&self,
out: &Tensor,
dim: i64,
keepdim: bool,
dtype: Kind
) -> Tensor
pub fn put(&self, index: &Tensor, source: &Tensor, accumulate: bool) -> Tensor
pub fn put_(
&mut self,
index: &Tensor,
source: &Tensor,
accumulate: bool
) -> Tensor
pub fn q_per_channel_axis(&self) -> i64
pub fn q_per_channel_scales(&self) -> Tensor
pub fn q_per_channel_zero_points(&self) -> Tensor
pub fn q_scale(&self) -> f64
pub fn q_zero_point(&self) -> i64
pub fn qr(&self, some: bool) -> (Tensor, Tensor)
pub fn qr_q(&self, q: &Tensor, r: &Tensor, some: bool) -> (Tensor, Tensor)
pub fn quantile(
&self,
q: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Tensor
pub fn quantile_out(
&self,
out: &Tensor,
q: &Tensor,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Tensor
pub fn quantile_scalar(
&self,
q: f64,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Tensor
pub fn quantile_scalar_out(
&self,
out: &Tensor,
q: f64,
dim: impl Into<Option<i64>>,
keepdim: bool,
interpolation: &str
) -> Tensor
pub fn quantize_per_channel(
&self,
scales: &Tensor,
zero_points: &Tensor,
axis: i64,
dtype: Kind
) -> Tensor
pub fn quantize_per_tensor(
&self,
scale: f64,
zero_point: i64,
dtype: Kind
) -> Tensor
pub fn quantize_per_tensor_dynamic(
&self,
dtype: Kind,
reduce_range: bool
) -> Tensor
pub fn quantize_per_tensor_tensor_qparams(
&self,
scale: &Tensor,
zero_point: &Tensor,
dtype: Kind
) -> Tensor
pub fn quantized_batch_norm<T>(
&self,
weight: Option<T>,
bias: Option<T>,
mean: &Tensor,
var: &Tensor,
eps: f64,
output_scale: f64,
output_zero_point: i64
) -> Tensor where
T: Borrow<Tensor>,
pub fn quantized_gru_cell<S>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: &Tensor,
b_hh: &Tensor,
packed_ih: &Tensor,
packed_hh: &Tensor,
col_offsets_ih: &Tensor,
col_offsets_hh: &Tensor,
scale_ih: S,
scale_hh: S,
zero_point_ih: S,
zero_point_hh: S
) -> Tensor where
S: Into<Scalar>,
pub fn quantized_lstm_cell<T, S>(
&self,
hx: &[T],
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: &Tensor,
b_hh: &Tensor,
packed_ih: &Tensor,
packed_hh: &Tensor,
col_offsets_ih: &Tensor,
col_offsets_hh: &Tensor,
scale_ih: S,
scale_hh: S,
zero_point_ih: S,
zero_point_hh: S
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
S: Into<Scalar>,
pub fn quantized_max_pool1d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Tensor
pub fn quantized_max_pool2d(
&self,
kernel_size: &[i64],
stride: &[i64],
padding: &[i64],
dilation: &[i64],
ceil_mode: bool
) -> Tensor
pub fn quantized_rnn_relu_cell<S>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: &Tensor,
b_hh: &Tensor,
packed_ih: &Tensor,
packed_hh: &Tensor,
col_offsets_ih: &Tensor,
col_offsets_hh: &Tensor,
scale_ih: S,
scale_hh: S,
zero_point_ih: S,
zero_point_hh: S
) -> Tensor where
S: Into<Scalar>,
pub fn quantized_rnn_tanh_cell<S>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: &Tensor,
b_hh: &Tensor,
packed_ih: &Tensor,
packed_hh: &Tensor,
col_offsets_ih: &Tensor,
col_offsets_hh: &Tensor,
scale_ih: S,
scale_hh: S,
zero_point_ih: S,
zero_point_hh: S
) -> Tensor where
S: Into<Scalar>,
pub fn rad2deg(&self) -> Tensor
pub fn rad2deg_(&mut self) -> Tensor
pub fn rad2deg_out(&self, out: &Tensor) -> Tensor
pub fn rand_like(&self) -> Tensor
pub fn randint_like(&self, high: i64) -> Tensor
pub fn randint_like_low_dtype(&self, low: i64, high: i64) -> Tensor
pub fn randn_like(&self) -> Tensor
pub fn random_(&mut self) -> Tensor
pub fn random_from_(&mut self, from: i64, to: impl Into<Option<i64>>) -> Tensor
pub fn random_to_(&mut self, to: i64) -> Tensor
pub fn ravel(&self) -> Tensor
pub fn real(&self) -> Tensor
pub fn reciprocal(&self) -> Tensor
pub fn reciprocal_(&mut self) -> Tensor
pub fn reciprocal_out(&self, out: &Tensor) -> Tensor
pub fn reflection_pad1d(&self, padding: &[i64]) -> Tensor
pub fn reflection_pad1d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn reflection_pad1d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn reflection_pad1d_out(&self, out: &Tensor, padding: &[i64]) -> Tensor
pub fn reflection_pad2d(&self, padding: &[i64]) -> Tensor
pub fn reflection_pad2d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn reflection_pad2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn reflection_pad2d_out(&self, out: &Tensor, padding: &[i64]) -> Tensor
pub fn reflection_pad3d(&self, padding: &[i64]) -> Tensor
pub fn reflection_pad3d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn reflection_pad3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn reflection_pad3d_out(&self, out: &Tensor, padding: &[i64]) -> Tensor
pub fn relu(&self) -> Tensor
pub fn relu6(&self) -> Tensor
pub fn relu6_(&mut self) -> Tensor
pub fn relu_(&mut self) -> Tensor
pub fn remainder<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn remainder_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn remainder_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn remainder_tensor(&self, other: &Tensor) -> Tensor
pub fn remainder_tensor_(&mut self, other: &Tensor) -> Tensor
pub fn remainder_tensor_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn renorm<S>(&self, p: S, dim: i64, maxnorm: S) -> Tensor where
S: Into<Scalar>,
pub fn renorm_<S>(&mut self, p: S, dim: i64, maxnorm: S) -> Tensor where
S: Into<Scalar>,
pub fn renorm_out<S>(&self, out: &Tensor, p: S, dim: i64, maxnorm: S) -> Tensor where
S: Into<Scalar>,
pub fn repeat(&self, repeats: &[i64]) -> Tensor
pub fn repeat_interleave_self_int(
&self,
repeats: i64,
dim: impl Into<Option<i64>>,
output_size: impl Into<Option<i64>>
) -> Tensor
pub fn repeat_interleave_self_tensor(
&self,
repeats: &Tensor,
dim: impl Into<Option<i64>>,
output_size: impl Into<Option<i64>>
) -> Tensor
pub fn replication_pad1d(&self, padding: &[i64]) -> Tensor
pub fn replication_pad1d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn replication_pad1d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn replication_pad1d_out(&self, out: &Tensor, padding: &[i64]) -> Tensor
pub fn replication_pad2d(&self, padding: &[i64]) -> Tensor
pub fn replication_pad2d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn replication_pad2d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn replication_pad2d_out(&self, out: &Tensor, padding: &[i64]) -> Tensor
pub fn replication_pad3d(&self, padding: &[i64]) -> Tensor
pub fn replication_pad3d_backward(
&self,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn replication_pad3d_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
padding: &[i64]
) -> Tensor
pub fn replication_pad3d_out(&self, out: &Tensor, padding: &[i64]) -> Tensor
pub fn requires_grad_(&mut self, requires_grad: bool) -> Tensor
pub fn reshape(&self, shape: &[i64]) -> Tensor
pub fn reshape_as(&self, other: &Tensor) -> Tensor
pub fn resize_(&mut self, size: &[i64]) -> Tensor
pub fn resize_as_(&mut self, the_template: &Tensor) -> Tensor
pub fn resize_as_sparse_(&mut self, the_template: &Tensor) -> Tensor
pub fn resolve_conj(&self) -> Tensor
pub fn resolve_neg(&self) -> Tensor
pub fn retains_grad(&self) -> bool
pub fn rnn_relu<T>(
&self,
hx: &Tensor,
params: &[T],
has_biases: bool,
num_layers: i64,
dropout: f64,
train: bool,
bidirectional: bool,
batch_first: bool
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn rnn_relu_cell<T>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: Option<T>,
b_hh: Option<T>
) -> Tensor where
T: Borrow<Tensor>,
pub fn rnn_tanh<T>(
&self,
hx: &Tensor,
params: &[T],
has_biases: bool,
num_layers: i64,
dropout: f64,
train: bool,
bidirectional: bool,
batch_first: bool
) -> (Tensor, Tensor) where
T: Borrow<Tensor>,
pub fn rnn_tanh_cell<T>(
&self,
hx: &Tensor,
w_ih: &Tensor,
w_hh: &Tensor,
b_ih: Option<T>,
b_hh: Option<T>
) -> Tensor where
T: Borrow<Tensor>,
pub fn roll(&self, shifts: &[i64], dims: &[i64]) -> Tensor
pub fn rot90(&self, k: i64, dims: &[i64]) -> Tensor
pub fn round(&self) -> Tensor
pub fn round_(&mut self) -> Tensor
pub fn round_decimals(&self, decimals: i64) -> Tensor
pub fn round_decimals_(&mut self, decimals: i64) -> Tensor
pub fn round_decimals_out(&self, out: &Tensor, decimals: i64) -> Tensor
pub fn round_out(&self, out: &Tensor) -> Tensor
pub fn rrelu(&self, training: bool) -> Tensor
pub fn rrelu_(&mut self, training: bool) -> Tensor
pub fn rrelu_with_noise(&self, noise: &Tensor, training: bool) -> Tensor
pub fn rrelu_with_noise_(&mut self, noise: &Tensor, training: bool) -> Tensor
pub fn rrelu_with_noise_backward<S>(
&self,
grad_output: &Tensor,
noise: &Tensor,
lower: S,
upper: S,
training: bool,
self_is_result: bool
) -> Tensor where
S: Into<Scalar>,
pub fn rrelu_with_noise_out(
&self,
out: &Tensor,
noise: &Tensor,
training: bool
) -> Tensor
pub fn rsqrt(&self) -> Tensor
pub fn rsqrt_(&mut self) -> Tensor
pub fn rsqrt_out(&self, out: &Tensor) -> Tensor
pub fn rsub(&self, other: &Tensor) -> Tensor
pub fn rsub_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn scatter(&self, dim: i64, index: &Tensor, src: &Tensor) -> Tensor
pub fn scatter_(&mut self, dim: i64, index: &Tensor, src: &Tensor) -> Tensor
pub fn scatter_add(&self, dim: i64, index: &Tensor, src: &Tensor) -> Tensor
pub fn scatter_add_(&mut self, dim: i64, index: &Tensor, src: &Tensor) -> Tensor
pub fn scatter_add_out(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
src: &Tensor
) -> Tensor
pub fn scatter_reduce(
&self,
dim: i64,
index: &Tensor,
src: &Tensor,
reduce: &str
) -> Tensor
pub fn scatter_reduce_(
&mut self,
dim: i64,
index: &Tensor,
src: &Tensor,
reduce: &str
) -> Tensor
pub fn scatter_reduce_out(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
src: &Tensor,
reduce: &str
) -> Tensor
pub fn scatter_src_out(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
src: &Tensor
) -> Tensor
pub fn scatter_value<S>(&self, dim: i64, index: &Tensor, value: S) -> Tensor where
S: Into<Scalar>,
pub fn scatter_value_<S>(
&mut self,
dim: i64,
index: &Tensor,
value: S
) -> Tensor where
S: Into<Scalar>,
pub fn scatter_value_out<S>(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
value: S
) -> Tensor where
S: Into<Scalar>,
pub fn scatter_value_reduce<S>(
&self,
dim: i64,
index: &Tensor,
value: S,
reduce: &str
) -> Tensor where
S: Into<Scalar>,
pub fn scatter_value_reduce_<S>(
&mut self,
dim: i64,
index: &Tensor,
value: S,
reduce: &str
) -> Tensor where
S: Into<Scalar>,
pub fn scatter_value_reduce_out<S>(
&self,
out: &Tensor,
dim: i64,
index: &Tensor,
value: S,
reduce: &str
) -> Tensor where
S: Into<Scalar>,
pub fn searchsorted<T>(
&self,
sorted_sequence: &Tensor,
out_int32: bool,
right: bool,
side: &str,
sorter: Option<T>
) -> Tensor where
T: Borrow<Tensor>,
pub fn searchsorted_tensor_out<T>(
&self,
out: &Tensor,
sorted_sequence: &Tensor,
out_int32: bool,
right: bool,
side: &str,
sorter: Option<T>
) -> Tensor where
T: Borrow<Tensor>,
pub fn select(&self, dim: i64, index: i64) -> Tensor
pub fn select_scatter(&self, src: &Tensor, dim: i64, index: i64) -> Tensor
pub fn selu(&self) -> Tensor
pub fn selu_(&mut self) -> Tensor
pub fn set_(&mut self) -> Tensor
pub fn set_data(&mut self, new_data: &Tensor)
pub fn set_requires_grad(&self, r: bool) -> Tensor
pub fn set_source_tensor_(&mut self, source: &Tensor) -> Tensor
pub fn sgn(&self) -> Tensor
pub fn sgn_(&mut self) -> Tensor
pub fn sgn_out(&self, out: &Tensor) -> Tensor
pub fn sigmoid(&self) -> Tensor
pub fn sigmoid_(&mut self) -> Tensor
pub fn sigmoid_out(&self, out: &Tensor) -> Tensor
pub fn sign(&self) -> Tensor
pub fn sign_(&mut self) -> Tensor
pub fn sign_out(&self, out: &Tensor) -> Tensor
pub fn signbit(&self) -> Tensor
pub fn signbit_out(&self, out: &Tensor) -> Tensor
pub fn silu(&self) -> Tensor
pub fn silu_(&mut self) -> Tensor
pub fn silu_backward(&self, grad_output: &Tensor) -> Tensor
pub fn silu_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor
) -> Tensor
pub fn silu_out(&self, out: &Tensor) -> Tensor
pub fn sin(&self) -> Tensor
pub fn sin_(&mut self) -> Tensor
pub fn sin_out(&self, out: &Tensor) -> Tensor
pub fn sinc(&self) -> Tensor
pub fn sinc_(&mut self) -> Tensor
pub fn sinc_out(&self, out: &Tensor) -> Tensor
pub fn sinh(&self) -> Tensor
pub fn sinh_(&mut self) -> Tensor
pub fn sinh_out(&self, out: &Tensor) -> Tensor
pub fn slice(
&self,
dim: i64,
start: impl Into<Option<i64>>,
end: impl Into<Option<i64>>,
step: i64
) -> Tensor
pub fn slice_scatter(
&self,
src: &Tensor,
dim: i64,
start: impl Into<Option<i64>>,
end: impl Into<Option<i64>>,
step: i64
) -> Tensor
pub fn slogdet(&self) -> (Tensor, Tensor)
pub fn slow_conv3d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn slow_conv3d_out<T>(
&self,
out: &Tensor,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn slow_conv_dilated2d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn slow_conv_dilated3d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn slow_conv_transpose2d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn slow_conv_transpose2d_out<T>(
&self,
out: &Tensor,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn slow_conv_transpose3d<T>(
&self,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn slow_conv_transpose3d_out<T>(
&self,
out: &Tensor,
weight: &Tensor,
kernel_size: &[i64],
bias: Option<T>,
stride: &[i64],
padding: &[i64],
output_padding: &[i64],
dilation: &[i64]
) -> Tensor where
T: Borrow<Tensor>,
pub fn smm(&self, mat2: &Tensor) -> Tensor
pub fn smooth_l1_loss(
&self,
target: &Tensor,
reduction: Reduction,
beta: f64
) -> Tensor
pub fn smooth_l1_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
beta: f64
) -> Tensor
pub fn smooth_l1_loss_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction,
beta: f64
) -> Tensor
pub fn smooth_l1_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction,
beta: f64
) -> Tensor
pub fn soft_margin_loss(&self, target: &Tensor, reduction: Reduction) -> Tensor
pub fn soft_margin_loss_backward(
&self,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Tensor
pub fn soft_margin_loss_backward_grad_input(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Tensor
pub fn soft_margin_loss_out(
&self,
out: &Tensor,
target: &Tensor,
reduction: Reduction
) -> Tensor
pub fn softmax(&self, dim: i64, dtype: Kind) -> Tensor
pub fn softplus(&self) -> Tensor
pub fn softplus_backward<S>(
&self,
grad_output: &Tensor,
beta: S,
threshold: S
) -> Tensor where
S: Into<Scalar>,
pub fn softplus_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
beta: S,
threshold: S
) -> Tensor where
S: Into<Scalar>,
pub fn softplus_out(&self, out: &Tensor) -> Tensor
pub fn softshrink(&self) -> Tensor
pub fn softshrink_backward<S>(&self, grad_output: &Tensor, lambd: S) -> Tensor where
S: Into<Scalar>,
pub fn softshrink_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
lambd: S
) -> Tensor where
S: Into<Scalar>,
pub fn softshrink_out(&self, out: &Tensor) -> Tensor
pub fn solve(&self, a: &Tensor) -> (Tensor, Tensor)
pub fn solve_solution(
&self,
solution: &Tensor,
lu: &Tensor,
a: &Tensor
) -> (Tensor, Tensor)
pub fn sort(&self, dim: i64, descending: bool) -> (Tensor, Tensor)
pub fn sort_stable(
&self,
stable: bool,
dim: i64,
descending: bool
) -> (Tensor, Tensor)
pub fn sort_values(
&self,
values: &Tensor,
indices: &Tensor,
dim: i64,
descending: bool
) -> (Tensor, Tensor)
pub fn sort_values_stable(
&self,
values: &Tensor,
indices: &Tensor,
stable: bool,
dim: i64,
descending: bool
) -> (Tensor, Tensor)
pub fn sparse_dim(&self) -> i64
pub fn sparse_mask(&self, mask: &Tensor) -> Tensor
pub fn sparse_resize_(
&mut self,
size: &[i64],
sparse_dim: i64,
dense_dim: i64
) -> Tensor
pub fn sparse_resize_and_clear_(
&mut self,
size: &[i64],
sparse_dim: i64,
dense_dim: i64
) -> Tensor
pub fn sparse_sampled_addmm(&self, mat1: &Tensor, mat2: &Tensor) -> Tensor
pub fn sparse_sampled_addmm_out(
&self,
out: &Tensor,
mat1: &Tensor,
mat2: &Tensor
) -> Tensor
pub fn special_digamma(&self) -> Tensor
pub fn special_digamma_out(&self, out: &Tensor) -> Tensor
pub fn special_entr(&self) -> Tensor
pub fn special_entr_out(&self, out: &Tensor) -> Tensor
pub fn special_erf(&self) -> Tensor
pub fn special_erf_out(&self, out: &Tensor) -> Tensor
pub fn special_erfc(&self) -> Tensor
pub fn special_erfc_out(&self, out: &Tensor) -> Tensor
pub fn special_erfcx(&self) -> Tensor
pub fn special_erfcx_out(&self, out: &Tensor) -> Tensor
pub fn special_erfinv(&self) -> Tensor
pub fn special_erfinv_out(&self, out: &Tensor) -> Tensor
pub fn special_exp2(&self) -> Tensor
pub fn special_exp2_out(&self, out: &Tensor) -> Tensor
pub fn special_expit(&self) -> Tensor
pub fn special_expit_out(&self, out: &Tensor) -> Tensor
pub fn special_expm1(&self) -> Tensor
pub fn special_expm1_out(&self, out: &Tensor) -> Tensor
pub fn special_gammainc(&self, other: &Tensor) -> Tensor
pub fn special_gammainc_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn special_gammaincc(&self, other: &Tensor) -> Tensor
pub fn special_gammaincc_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn special_gammaln(&self) -> Tensor
pub fn special_gammaln_out(&self, out: &Tensor) -> Tensor
pub fn special_i0(&self) -> Tensor
pub fn special_i0_out(&self, out: &Tensor) -> Tensor
pub fn special_i0e(&self) -> Tensor
pub fn special_i0e_out(&self, out: &Tensor) -> Tensor
pub fn special_i1(&self) -> Tensor
pub fn special_i1_out(&self, out: &Tensor) -> Tensor
pub fn special_i1e(&self) -> Tensor
pub fn special_i1e_out(&self, out: &Tensor) -> Tensor
pub fn special_log1p(&self) -> Tensor
pub fn special_log1p_out(&self, out: &Tensor) -> Tensor
pub fn special_log_softmax(&self, dim: i64, dtype: Kind) -> Tensor
pub fn special_logit(&self, eps: impl Into<Option<f64>>) -> Tensor
pub fn special_logit_out(
&self,
out: &Tensor,
eps: impl Into<Option<f64>>
) -> Tensor
pub fn special_logsumexp(&self, dim: &[i64], keepdim: bool) -> Tensor
pub fn special_logsumexp_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool
) -> Tensor
pub fn special_multigammaln(&self, p: i64) -> Tensor
pub fn special_multigammaln_out(&self, out: &Tensor, p: i64) -> Tensor
pub fn special_ndtr(&self) -> Tensor
pub fn special_ndtr_out(&self, out: &Tensor) -> Tensor
pub fn special_ndtri(&self) -> Tensor
pub fn special_ndtri_out(&self, out: &Tensor) -> Tensor
pub fn special_polygamma(&self, n: i64) -> Tensor
pub fn special_polygamma_out(&self, out: &Tensor, n: i64) -> Tensor
pub fn special_psi(&self) -> Tensor
pub fn special_psi_out(&self, out: &Tensor) -> Tensor
pub fn special_round(&self, decimals: i64) -> Tensor
pub fn special_round_out(&self, out: &Tensor, decimals: i64) -> Tensor
pub fn special_sinc(&self) -> Tensor
pub fn special_sinc_out(&self, out: &Tensor) -> Tensor
pub fn special_softmax(&self, dim: i64, dtype: Kind) -> Tensor
pub fn special_xlog1py(&self, other: &Tensor) -> Tensor
pub fn special_xlog1py_other_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn special_xlog1py_other_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Tensor where
S: Into<Scalar>,
pub fn special_xlog1py_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn special_xlogy(&self, other: &Tensor) -> Tensor
pub fn special_xlogy_other_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn special_xlogy_other_scalar_out<S>(
&self,
out: &Tensor,
other: S
) -> Tensor where
S: Into<Scalar>,
pub fn special_xlogy_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn special_zeta(&self, other: &Tensor) -> Tensor
pub fn special_zeta_other_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn special_zeta_other_scalar_out<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn special_zeta_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn split(&self, split_size: i64, dim: i64) -> Vec<Tensor, Global>
pub fn split_with_sizes(
&self,
split_sizes: &[i64],
dim: i64
) -> Vec<Tensor, Global>
pub fn sqrt(&self) -> Tensor
pub fn sqrt_(&mut self) -> Tensor
pub fn sqrt_out(&self, out: &Tensor) -> Tensor
pub fn square(&self) -> Tensor
pub fn square_(&mut self) -> Tensor
pub fn square_out(&self, out: &Tensor) -> Tensor
pub fn squeeze(&self) -> Tensor
pub fn squeeze_(&mut self) -> Tensor
pub fn squeeze_dim(&self, dim: i64) -> Tensor
pub fn squeeze_dim_(&mut self, dim: i64) -> Tensor
pub fn sspaddmm(&self, mat1: &Tensor, mat2: &Tensor) -> Tensor
pub fn sspaddmm_out(&self, out: &Tensor, mat1: &Tensor, mat2: &Tensor) -> Tensor
pub fn std(&self, unbiased: bool) -> Tensor
pub fn std_correction<'a>(
&self,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> Tensor
pub fn std_correction_out<'a>(
&self,
out: &Tensor,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> Tensor
pub fn std_dim(&self, dim: &[i64], unbiased: bool, keepdim: bool) -> Tensor
pub fn std_mean(&self, unbiased: bool) -> (Tensor, Tensor)
pub fn std_mean_correction<'a>(
&self,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> (Tensor, Tensor)
pub fn std_mean_dim(
&self,
dim: &[i64],
unbiased: bool,
keepdim: bool
) -> (Tensor, Tensor)
pub fn std_out(
&self,
out: &Tensor,
dim: &[i64],
unbiased: bool,
keepdim: bool
) -> Tensor
pub fn stft<T>(
&self,
n_fft: i64,
hop_length: impl Into<Option<i64>>,
win_length: impl Into<Option<i64>>,
window: Option<T>,
normalized: bool,
onesided: bool,
return_complex: bool
) -> Tensor where
T: Borrow<Tensor>,
pub fn g_sub(&self, other: &Tensor) -> Tensor
pub fn g_sub_(&mut self, other: &Tensor) -> Tensor
pub fn sub_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn g_sub_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn g_sub_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn subtract(&self, other: &Tensor) -> Tensor
pub fn subtract_(&mut self, other: &Tensor) -> Tensor
pub fn subtract_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn subtract_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn subtract_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn sum(&self, dtype: Kind) -> Tensor
pub fn sum_dim_intlist(&self, dim: &[i64], keepdim: bool, dtype: Kind) -> Tensor
pub fn sum_intlist_out(
&self,
out: &Tensor,
dim: &[i64],
keepdim: bool,
dtype: Kind
) -> Tensor
pub fn sum_to_size(&self, size: &[i64]) -> Tensor
pub fn svd(&self, some: bool, compute_uv: bool) -> (Tensor, Tensor, Tensor)
pub fn svd_u(
&self,
u: &Tensor,
s: &Tensor,
v: &Tensor,
some: bool,
compute_uv: bool
) -> (Tensor, Tensor, Tensor)
pub fn swapaxes(&self, axis0: i64, axis1: i64) -> Tensor
pub fn swapaxes_(&mut self, axis0: i64, axis1: i64) -> Tensor
pub fn swapdims(&self, dim0: i64, dim1: i64) -> Tensor
pub fn swapdims_(&mut self, dim0: i64, dim1: i64) -> Tensor
pub fn symeig(&self, eigenvectors: bool, upper: bool) -> (Tensor, Tensor)
pub fn symeig_e(
&self,
e: &Tensor,
v: &Tensor,
eigenvectors: bool,
upper: bool
) -> (Tensor, Tensor)
pub fn tr(&self) -> Tensor
pub fn t_(&mut self) -> Tensor
pub fn take(&self, index: &Tensor) -> Tensor
pub fn take_along_dim(
&self,
indices: &Tensor,
dim: impl Into<Option<i64>>
) -> Tensor
pub fn take_along_dim_out(
&self,
out: &Tensor,
indices: &Tensor,
dim: impl Into<Option<i64>>
) -> Tensor
pub fn take_out(&self, out: &Tensor, index: &Tensor) -> Tensor
pub fn tan(&self) -> Tensor
pub fn tan_(&mut self) -> Tensor
pub fn tan_out(&self, out: &Tensor) -> Tensor
pub fn tanh(&self) -> Tensor
pub fn tanh_(&mut self) -> Tensor
pub fn tanh_out(&self, out: &Tensor) -> Tensor
pub fn tensor_split(&self, sections: i64, dim: i64) -> Vec<Tensor, Global>
pub fn tensor_split_indices(
&self,
indices: &[i64],
dim: i64
) -> Vec<Tensor, Global>
pub fn tensor_split_tensor_indices_or_sections(
&self,
tensor_indices_or_sections: &Tensor,
dim: i64
) -> Vec<Tensor, Global>
pub fn tensordot(
&self,
other: &Tensor,
dims_self: &[i64],
dims_other: &[i64]
) -> Tensor
pub fn tensordot_out(
&self,
out: &Tensor,
other: &Tensor,
dims_self: &[i64],
dims_other: &[i64]
) -> Tensor
pub fn threshold<S>(&self, threshold: S, value: S) -> Tensor where
S: Into<Scalar>,
pub fn threshold_<S>(&mut self, threshold: S, value: S) -> Tensor where
S: Into<Scalar>,
pub fn threshold_backward<S>(
&self,
grad_output: &Tensor,
threshold: S
) -> Tensor where
S: Into<Scalar>,
pub fn threshold_backward_grad_input<S>(
&self,
grad_input: &Tensor,
grad_output: &Tensor,
threshold: S
) -> Tensor where
S: Into<Scalar>,
pub fn threshold_out<S>(&self, out: &Tensor, threshold: S, value: S) -> Tensor where
S: Into<Scalar>,
pub fn tile(&self, dims: &[i64]) -> Tensor
pub fn to(&self, device: Device) -> Tensor
pub fn to_dense(&self, dtype: Kind) -> Tensor
pub fn to_dense_backward(&self, grad: &Tensor) -> Tensor
pub fn to_device_(
&self,
device: Device,
dtype: Kind,
non_blocking: bool,
copy: bool
) -> Tensor
pub fn to_dtype(&self, dtype: Kind, non_blocking: bool, copy: bool) -> Tensor
pub fn to_dtype_layout(
&self,
options: (Kind, Device),
non_blocking: bool,
copy: bool
) -> Tensor
pub fn g_to_mkldnn(&self, dtype: Kind) -> Tensor
pub fn to_mkldnn_backward(&self, grad: &Tensor) -> Tensor
pub fn to_other(&self, other: &Tensor, non_blocking: bool, copy: bool) -> Tensor
pub fn to_sparse(&self) -> Tensor
pub fn to_sparse_sparse_dim(&self, sparse_dim: i64) -> Tensor
pub fn topk(
&self,
k: i64,
dim: i64,
largest: bool,
sorted: bool
) -> (Tensor, Tensor)
pub fn topk_values(
&self,
values: &Tensor,
indices: &Tensor,
k: i64,
dim: i64,
largest: bool,
sorted: bool
) -> (Tensor, Tensor)
pub fn totype(&self, scalar_type: Kind) -> Tensor
pub fn trace(&self) -> Tensor
pub fn transpose(&self, dim0: i64, dim1: i64) -> Tensor
pub fn transpose_(&mut self, dim0: i64, dim1: i64) -> Tensor
pub fn triangular_solve(
&self,
a: &Tensor,
upper: bool,
transpose: bool,
unitriangular: bool
) -> (Tensor, Tensor)
pub fn triangular_solve_x(
&self,
x: &Tensor,
m: &Tensor,
a: &Tensor,
upper: bool,
transpose: bool,
unitriangular: bool
) -> (Tensor, Tensor)
pub fn tril(&self, diagonal: i64) -> Tensor
pub fn tril_(&mut self, diagonal: i64) -> Tensor
pub fn tril_out(&self, out: &Tensor, diagonal: i64) -> Tensor
pub fn triu(&self, diagonal: i64) -> Tensor
pub fn triu_(&mut self, diagonal: i64) -> Tensor
pub fn triu_out(&self, out: &Tensor, diagonal: i64) -> Tensor
pub fn true_divide(&self, other: &Tensor) -> Tensor
pub fn true_divide_(&mut self, other: &Tensor) -> Tensor
pub fn true_divide_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn true_divide_scalar<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn true_divide_scalar_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn trunc(&self) -> Tensor
pub fn trunc_(&mut self) -> Tensor
pub fn trunc_out(&self, out: &Tensor) -> Tensor
pub fn type_as(&self, other: &Tensor) -> Tensor
pub fn unbind(&self, dim: i64) -> Vec<Tensor, Global>
pub fn unflatten(&self, dim: i64, sizes: &[i64]) -> Tensor
pub fn unfold(&self, dimension: i64, size: i64, step: i64) -> Tensor
pub fn uniform_(&mut self, from: f64, to: f64) -> Tensor
pub fn unique_consecutive(
&self,
return_inverse: bool,
return_counts: bool,
dim: impl Into<Option<i64>>
) -> (Tensor, Tensor, Tensor)
pub fn unique_dim(
&self,
dim: i64,
sorted: bool,
return_inverse: bool,
return_counts: bool
) -> (Tensor, Tensor, Tensor)
pub fn unique_dim_consecutive(
&self,
dim: i64,
return_inverse: bool,
return_counts: bool
) -> (Tensor, Tensor, Tensor)
pub fn unsafe_chunk(&self, chunks: i64, dim: i64) -> Vec<Tensor, Global>
pub fn unsafe_split(&self, split_size: i64, dim: i64) -> Vec<Tensor, Global>
pub fn unsafe_split_with_sizes(
&self,
split_sizes: &[i64],
dim: i64
) -> Vec<Tensor, Global>
pub fn unsqueeze(&self, dim: i64) -> Tensor
pub fn unsqueeze_(&mut self, dim: i64) -> Tensor
pub fn upsample_bicubic2d(
&self,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_bicubic2d_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_bicubic2d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Tensor
pub fn upsample_bilinear2d(
&self,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_bilinear2d_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_bilinear2d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Tensor
pub fn upsample_linear1d(
&self,
output_size: &[i64],
align_corners: bool,
scales: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_linear1d_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_linear1d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Tensor
pub fn upsample_nearest1d(
&self,
output_size: &[i64],
scales: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_nearest1d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_nearest1d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Tensor
pub fn upsample_nearest2d(
&self,
output_size: &[i64],
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_nearest2d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_nearest2d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Tensor
pub fn upsample_nearest3d(
&self,
output_size: &[i64],
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_nearest3d_out(
&self,
out: &Tensor,
output_size: &[i64],
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_nearest3d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
scale_factors: &[f64]
) -> Tensor
pub fn upsample_trilinear3d(
&self,
output_size: &[i64],
align_corners: bool,
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_trilinear3d_out(
&self,
out: &Tensor,
output_size: &[i64],
align_corners: bool,
scales_d: impl Into<Option<f64>>,
scales_h: impl Into<Option<f64>>,
scales_w: impl Into<Option<f64>>
) -> Tensor
pub fn upsample_trilinear3d_vec<'a>(
&self,
output_size: impl Into<Option<&'a [i64]>>,
align_corners: bool,
scale_factors: &[f64]
) -> Tensor
pub fn values(&self) -> Tensor
pub fn var(&self, unbiased: bool) -> Tensor
pub fn var_correction<'a>(
&self,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> Tensor
pub fn var_correction_out<'a>(
&self,
out: &Tensor,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> Tensor
pub fn var_dim(&self, dim: &[i64], unbiased: bool, keepdim: bool) -> Tensor
pub fn var_mean(&self, unbiased: bool) -> (Tensor, Tensor)
pub fn var_mean_correction<'a>(
&self,
dim: impl Into<Option<&'a [i64]>>,
correction: impl Into<Option<i64>>,
keepdim: bool
) -> (Tensor, Tensor)
pub fn var_mean_dim(
&self,
dim: &[i64],
unbiased: bool,
keepdim: bool
) -> (Tensor, Tensor)
pub fn var_out(
&self,
out: &Tensor,
dim: &[i64],
unbiased: bool,
keepdim: bool
) -> Tensor
pub fn vdot(&self, other: &Tensor) -> Tensor
pub fn vdot_out(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn view_(&self, size: &[i64]) -> Tensor
pub fn view_as(&self, other: &Tensor) -> Tensor
pub fn view_as_complex(&self) -> Tensor
pub fn view_as_real(&self) -> Tensor
pub fn view_dtype(&self, dtype: Kind) -> Tensor
pub fn vsplit(&self, sections: i64) -> Vec<Tensor, Global>
pub fn vsplit_array(&self, indices: &[i64]) -> Vec<Tensor, Global>
pub fn where_scalarother<S>(&self, condition: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn where_self(&self, condition: &Tensor, other: &Tensor) -> Tensor
pub fn xlogy(&self, other: &Tensor) -> Tensor
pub fn xlogy_(&mut self, other: &Tensor) -> Tensor
pub fn xlogy_outscalar_other<S>(&self, out: &Tensor, other: S) -> Tensor where
S: Into<Scalar>,
pub fn xlogy_outtensor(&self, out: &Tensor, other: &Tensor) -> Tensor
pub fn xlogy_scalar_other<S>(&self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn xlogy_scalar_other_<S>(&mut self, other: S) -> Tensor where
S: Into<Scalar>,
pub fn zero_(&mut self) -> Tensor
pub fn zeros_like(&self) -> Tensor
pub fn f_view<T>(&self, s: T) -> Result<Tensor, TchError> where
T: Shape,
pub fn view<T>(&self, s: T) -> Tensor where
T: Shape,
pub fn f_zero_pad1d(&self, left: i64, right: i64) -> Result<Tensor, TchError>
pub fn zero_pad1d(&self, left: i64, right: i64) -> Tensor
pub fn f_zero_pad2d(
&self,
left: i64,
right: i64,
top: i64,
bottom: i64
) -> Result<Tensor, TchError>
pub fn zero_pad2d(&self, left: i64, right: i64, top: i64, bottom: i64) -> Tensor
pub fn f_to_kind(&self, kind: Kind) -> Result<Tensor, TchError>
pub fn nll_loss(&self, targets: &Tensor) -> Tensor
sourcepub fn cross_entropy_for_logits(&self, targets: &Tensor) -> Tensor
pub fn cross_entropy_for_logits(&self, targets: &Tensor) -> Tensor
Computes the cross-entropy loss based on some logits and targets.
sourcepub fn accuracy_for_logits(&self, targets: &Tensor) -> Tensor
pub fn accuracy_for_logits(&self, targets: &Tensor) -> Tensor
Returns the average accuracy for some given logits assuming that targets represent ground-truth.
pub fn random_batch(&self, batch_size: i64) -> Tensor
pub fn f_to_device(&self, device: Device) -> Result<Tensor, TchError>
pub fn avg_pool2d_default(&self, ksize: i64) -> Tensor
pub fn max_pool2d_default(&self, ksize: i64) -> Tensor
sourcepub fn flat_view(&self) -> Tensor
pub fn flat_view(&self) -> Tensor
Flattens a tensor.
This returns a flattened version of the given tensor. The first dimension is preserved as it is assumed to be the mini-batch dimension.
sourcepub fn onehot(&self, labels: i64) -> Tensor
pub fn onehot(&self, labels: i64) -> Tensor
Converts a tensor to a one-hot encoded version.
If the input has a size [N1, N2, …, Nk], the returned tensor has a size [N1, …, Nk, labels]. The returned tensor uses float values. Elements of the input vector are expected to be between 0 and labels-1.
sourcepub fn copy(&self) -> Tensor
pub fn copy(&self) -> Tensor
Copies a tensor to a newly allocated tensor using the same shape and device.
pub fn to_mkldnn(&self) -> Tensor
pub fn apply<M>(&self, m: &M) -> Tensor where
M: Module,
pub fn apply_t<M>(&self, m: &M, train: bool) -> Tensor where
M: ModuleT,
pub fn apply_opt<M>(&self, m: &Option<M>) -> Tensor where
M: Module,
pub fn apply_opt_t<M>(&self, m: &Option<M>, train: bool) -> Tensor where
M: ModuleT,
Trait Implementations
sourceimpl<'a> AsRef<Tensor> for OpenCvMatAsTchTensor<'a>
impl<'a> AsRef<Tensor> for OpenCvMatAsTchTensor<'a>
sourceimpl<'a> Debug for OpenCvMatAsTchTensor<'a>
impl<'a> Debug for OpenCvMatAsTchTensor<'a>
sourceimpl<'a> Deref for OpenCvMatAsTchTensor<'a>
impl<'a> Deref for OpenCvMatAsTchTensor<'a>
sourceimpl<'a> DerefMut for OpenCvMatAsTchTensor<'a>
impl<'a> DerefMut for OpenCvMatAsTchTensor<'a>
sourceimpl<'a> Drop for OpenCvMatAsTchTensor<'a>
impl<'a> Drop for OpenCvMatAsTchTensor<'a>
Auto Trait Implementations
impl<'a> RefUnwindSafe for OpenCvMatAsTchTensor<'a>
impl<'a> !Send for OpenCvMatAsTchTensor<'a>
impl<'a> !Sync for OpenCvMatAsTchTensor<'a>
impl<'a> Unpin for OpenCvMatAsTchTensor<'a>
impl<'a> UnwindSafe for OpenCvMatAsTchTensor<'a>
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
impl<T> Pointable for T
impl<T> Pointable for T
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
The inverse inclusion map: attempts to construct self
from the equivalent element of its
superset. Read more
fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
Checks if self
is actually part of its subset T
(and can be converted to it).
fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
Use with care! Same as self.to_subset
but without any property checks. Always succeeds.
fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts self
to the equivalent element of its superset.