Function data2vec_loss 

pub fn data2vec_loss(
    student_pred: &[f32],
    teacher_repr: &[f32],
    mask: &[bool],
    n_tokens: usize,
    dim: usize,
    config: &Data2VecConfig,
) -> SslResult<Data2VecResult>

Compute the data2vec loss for a single sample.

Implements the full algorithm:

1. Validate shapes.
2. Optionally L2-normalise teacher representations across masked tokens per feature dimension.
3. Compute mean Huber loss between student predictions and normalised teacher targets at masked positions only.

Arguments

• student_pred — [n_tokens × dim] student predictions (row-major).
• teacher_repr — [n_tokens × dim] teacher representations (row-major).
• mask — [n_tokens] boolean vector; true = masked position.
• n_tokens, dim — spatial and channel dimensions.
• config — data2vec hyper-parameters.

Errors

• SslError::EmptyInput when n_tokens == 0 or dim == 0.
• SslError::DimensionMismatch when any buffer has the wrong length.
• SslError::EmptyInput when no tokens are masked (graceful; returns 0.0 loss inside Data2VecResult rather than erroring, since the caller may legitimately supply an all-visible batch during warm-up).