onnx-ir 0.19.0

ONNX-IR is a pure Rust library for parsing ONNX models into an intermediate representation that can be used to generate code for various ML/DL frameworks
Documentation 
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use crate::ir::{ArgType, Node, TensorType};

/// Update output type for Flatten operation (rank 2).
pub fn flatten_update_outputs(node: &mut Node) {
    if node.inputs.len() != 1 {
        panic!("Flatten: multiple inputs are not supported");
    }
    let tensor = node
        .inputs
        .iter()
        .find_map(|input| match &input.ty {
            ArgType::Tensor(tensor) => Some(tensor),
            _ => None,
        })
        .unwrap();

    // Flatten to a 2D tensor
    node.outputs[0].ty = ArgType::Tensor(TensorType {
        rank: 2,
        ..tensor.clone()
    });
}

/// Create a FlattenConfig from the attributes of the node
pub fn flatten_config(curr: &Node) -> usize {
    // the begin dimension is the first dimension (Default: 1 per ONNX spec)
    let mut axis: i64 = 1;

    // check if the node has only one input
    if curr.inputs.len() != 1 {
        panic!(
            "Flatten: multiple inputs are not supported (got {:?})",
            curr.inputs.len()
        );
    }

    // extract the shape of the input tensor
    let tensor = match curr.inputs.first().unwrap().clone().ty {
        ArgType::Tensor(tensor) => tensor,
        _ => panic!("Only tensor input is valid"),
    };

    // check if the input tensor has at least 2 dimensions
    if tensor.rank < 2 {
        panic!(
            "Flatten: input tensor must have at least 2 dimensions (got {:?})",
            tensor.rank
        );
    }

    // extract the attributes
    for (key, value) in curr.attrs.iter() {
        if key.as_str() == "axis" {
            axis = value.clone().into_i64()
        }
    }

    // if beg_dim is negative, it is counted from the end
    if axis < 0 {
        axis += tensor.rank as i64;
    }

    axis as usize
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::ir::NodeType;
    use crate::node::test_utils::NodeBuilder;

    fn create_test_node(axis: i64) -> Node {
        NodeBuilder::new(NodeType::Flatten, "test_flatten")
            .input_tensor_f32("data", 4, None)
            .output_tensor_f32("output", 2, None)
            .attr_int("axis", axis)
            .build()
    }

    #[test]
    fn test_flatten_config_basic() {
        let node = create_test_node(1);
        let config = flatten_config(&node);
        assert_eq!(config, 1);
    }

    #[test]
    fn test_flatten_config_with_negative_axis() {
        let node = create_test_node(-2);
        let config = flatten_config(&node);
        assert_eq!(config, 2); // -2 + 4 = 2
    }

    #[test]
    #[should_panic(expected = "Flatten: input tensor must have at least 2 dimensions")]
    fn test_flatten_config_with_low_rank() {
        let mut node = create_test_node(1);
        // Replace the input with one that has lower rank
        let input = NodeBuilder::new(NodeType::Identity, "temp")
            .input_tensor_f32("x", 1, None)
            .build()
            .inputs
            .pop()
            .unwrap();
        node.inputs[0] = input;
        let _ = flatten_config(&node);
    }

    #[test]
    #[should_panic(expected = "Flatten: multiple inputs are not supported")]
    fn test_flatten_config_with_multiple_inputs() {
        let mut node = create_test_node(1);
        // Add an extra input
        let extra_input = NodeBuilder::new(NodeType::Identity, "temp")
            .input_tensor_f32("extra", 1, None)
            .build()
            .inputs
            .pop()
            .unwrap();
        node.inputs.push(extra_input);
        let _ = flatten_config(&node);
    }
}