class_docstr: Two-dimensional convolutional layer.
  
  Parameters
  ----------
  input_size : tuple
      the size of the input.
  output_size : tuple
      the size of the output..
  strides : matrix-like
      stride of the cross-correlation kernel.
  kernel : matrix-like
      the cross-correlation kernel.
  activation : str or None
      activation function name
  input_index_mapper : IndexMapper or None
      map indexes from this layer index to the input layer index size
docstr: Returns an iterator over the kernel value and input index
  for the output at index `(out_d, out_r, out_c)`.
  
  Parameters
  ----------
  out_d : int
      the output depth.
  out_r : int
      the output row.
  out_c : int
      the output column.
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code: def kernel_with_input_indexes(self, out_d, out_r, out_c):
      """
      Returns an iterator over the kernel value and input index
      for the output at index `(out_d, out_r, out_c)`.
  
      Parameters
      ----------
      out_d : int
          the output depth.
      out_r : int
          the output row.
      out_c : int
          the output column.
      """
      for (
          k_d,
          k_r,
          k_c,
      ), input_index in self.kernel_index_with_input_indexes(
          out_d, out_r, out_c
      ):
          k_v = self.__kernel[out_d, k_d, k_r, k_c]
          yield k_v, input_index
  
imports: import itertools
  import numpy
label_desc: Use image node_id 2 for calling the ConvLayer2D obj's underlying member method code with example usage: obj.kernel_with_input_indexes(out_d, out_r, out_c) without return types