fix failed onnx tests

Context

This MR is intended to fix the failed onnx tests encountered in issue aidge_onnx#50 and issue aidge_onnx#46.

This includes the following problems:

• Flatten: missing python binding.
• And/Equal: A mistake was made, the operator Equal was named And.
• AvgPooling: unsupported attrs dilations and ceil_mode
• MaxPooling: unsupported attr dilations

Detailed major modifications

The following modifications were made:

• Add python binding for flatten
• Add Equal operator
• Add dilations attr to Maxpooling
• Add dilations and ceil_mode to AvgPooling

TODO

This MR would only fix the following:

• Add Flatten binding
• Add Equal Op
• AvgPooling: add dilations and ceil_mode attrs
• MaxPooling: add dilationsattr

include/aidge/operator/AvgPooling.hpp

@@ -55,11 +55,30 @@ enum class AvgPoolingAttr {
  *
  * The AvgPooling operation computes the average value within sliding windows of specified size
  * (kernel dimensions) over the input tensor. The stride dimensions determine how the window
- * moves across the input. This operation is commonly used in neural networks to reduce the spatial
- * dimensions while preserving features.
+ * moves across the input. The dilation parameter allows spacing between kernel elements, and 
+ * `ceil_mode` determines whether to use ceiling instead of floor when computing the output shape.
+ * This operation is commonly used in neural networks to reduce spatial dimensions while preserving features.
  *
  * @tparam DIM Number of dimensions for the pooling operation.
+ *
+ * ### Output Shape Calculation
+ * - If `ceil_mode` is false:
+ *   `output_size = floor((input_size - dilation * (kernel_size - 1) - 1) / stride + 1)`
+ * - If `ceil_mode` is true:
+ *   `output_size = ceil((input_size - dilation * (kernel_size - 1) - 1) / stride + 1)`
+ *
+ * @example Example usage:
+ * - Input shape: (1, 3, 32, 32) // Batch size 1, 3 channels, 32x32 spatial dimensions
+ * - KernelDims: (2, 2)
+ * - StrideDims: (2, 2)
+ * - Dilation: (1, 1)
+ * - CeilMode: false
+ * - Output shape: (1, 3, 16, 16)
+ *
+ * @see OperatorTensor
+ * @see Registrable
  */
+
 template <DimIdx_t DIM>
 class AvgPooling_Op : public OperatorTensor,
                 public Registrable<AvgPooling_Op<DIM>, std::string, std::function<std::shared_ptr<OperatorImpl>(const AvgPooling_Op<DIM> &)>> {
@@ -95,9 +114,11 @@ public:
 
     /**
      * @brief Constructs an AvgPooling operation with specified kernel and stride dimensions.
-     * @param kernel_dims Size of the pooling window for each spatial dimension.
-     * @param stride_dims Step size (stride) for sliding the pooling window across the input dimensions.
+     * @param[in] kernel_dims Size of the pooling window for each spatial dimension.
+     * @param[in] stride_dims Step size (stride) for sliding the pooling window across the input dimensions.
      * Defaults to 1 for each dimension.
+     * @param[in] dilations Spatial dilations for the pooling operation.
+     * @param[in] ceil_mode Indicates whether to use ceil mode for output size calculation.
      */
     constexpr AvgPooling_Op(const std::array<DimSize_t, DIM> &kernel_dims,
                             const std::array<DimSize_t, DIM> &stride_dims = create_array<DimSize_t, DIM>(1),
@@ -113,7 +134,7 @@ public:
 
     /**
      * @brief Copy-constructor.
-     * @param op AvgPooling_Op to copy.
+     * @param[in] op AvgPooling_Op to copy.
      * @details Copies the operator attributes and its output tensor(s), but not
      * its input tensors. The new operator has no associated input.
      */
@@ -127,16 +148,16 @@ public:
 
     /**
      * @brief Calculates the output dimensions based on the input dimensions and operator attributes.
-     * @param allowDataDependency If true, considers data-dependent operations. Defaults to false.
+     * @param[in] allowDataDependency If true, considers data-dependent operations. Defaults to false.
      * @return True if the dimensions are successfully calculated.
      */
     bool forwardDims(bool /*allowDataDependency*/ = false) override final;
 
     /**
      * @brief Computes the receptive field of the operator.
-     * @param firstEltDims Dimensions of the first element.
-     * @param outputDims Dimensions of the output tensor.
-     * @param outputIdx Index of the output tensor. Defaults to 0.
+     * @param[in] firstEltDims Dimensions of the first element.
+     * @param[in] outputDims Dimensions of the output tensor.
+     * @param[in] outputIdx Index of the output tensor. Defaults to 0.
      * @return A vector of pairs representing the receptive fields.
      */
     std::vector<std::pair<std::vector<DimSize_t>, std::vector<DimSize_t>>>
@@ -146,8 +167,8 @@ public:
 
     /**
      * @brief Sets the backend for the operation.
-     * @param name Name of the backend.
-     * @param device Device index. Defaults to 0.
+     * @param[in] name Name of the backend.
+     * @param[in] device Device index. Defaults to 0.
      */
     void setBackend(const std::string &name, DeviceIdx_t device = 0) override final;