code: def conv2d_backward_weight_python(
      dy_np,
      x_np,
      kernel_size,
      stride,
      padding,
      layout="NCHW",
      groups=1,
      channels=None,
  ):
      """Gradient of the conv2d op with respect to weight, in NCHW or NHWC layout.
  
      Parameters
      ----------
      dy_np : numpy.ndarray
          4-D with shape [batch, in_channel, out_height, out_width] for NCHW layout
  
      x_np : numpy.ndarray
          4-D with shape [batch, in_channel, in_height, in_width] for NCHW layout
  
      kernel_size : tuple of two ints
          Height and width of the weight
  
      stride : tuple of two ints
          Stride size, or [stride_height, stride_width]
  
      padding : tuple of two ints
          Spatial padding, or [pad_h, pad_w]
  
      layout: string
          Layout of dy_np and x_np
  
      groups: int
          Number of groups for grouped convolution.
  
      channels : int
          Number of output channels of this convolution.
  
      Returns
      -------
      dw_np : np.ndarray
          Tensor of shape [num_filter, in_channel, filter_height, filter_width] for NCHW layout,
          [num_filter, filter_height, filter_width, in_channel] for NHWC layout.
      """
      if layout == "NCHW":
          return conv2d_backward_weight_nchw_python(
              dy_np,
              x_np,
              kernel_size,
              stride,
              padding,
              groups,
              channels,
          )
  
      dw_np_oihw = conv2d_backward_weight_nchw_python(
          np.transpose(dy_np, [0, 3, 1, 2]),
          np.transpose(x_np, [0, 3, 1, 2]),
          kernel_size,
          stride,
          padding,
          groups,
          channels,
      )
      return np.transpose(dw_np_oihw, [0, 2, 3, 1])
  
label_desc: Use image node_id 2 for calling a global function with example usage: conv2d_backward_weight_python(dy_np, x_np, kernel_size, stride, padding, layout, groups, channels) and returns: np, conv2d_backward_weight_nchw_python