code: def conv2d_hwcn_python(a_np, w_np, stride, padding):
      """Convolution operator in HWCN layout.
  
      Parameters
      ----------
      a_np : numpy.ndarray
          4-D with shape [in_height, in_width, in_channel, batch]
  
      w_np : numpy.ndarray
          4-D with shape [filter_height, filter_width, in_channel, num_filter]
  
      stride : int or a list/tuple of two ints
          Stride size, or [stride_height, stride_width]
  
      padding : int or str or a list/tuple of 2 or 4 ints
          Padding size, or ['VALID', 'SAME'], or
          [pad_height, pad_width] for 2 ints, or
          [pad_top, pad_left, pad_bottom, pad_right] for 2 ints
  
      Returns
      -------
      b_np : np.ndarray
          4-D with shape [out_height, out_width, out_channel, batch]
      """
      in_height, in_width, in_channel, batch = a_np.shape
      kernel_h, kernel_w, _, num_filter = w_np.shape
      if isinstance(stride, int):
          stride_h = stride_w = stride
      else:
          stride_h, stride_w = stride
  
      pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(
          padding, (kernel_h, kernel_w)
      )
      pad_h = pad_top + pad_bottom
      pad_w = pad_left + pad_right
      # compute the output shape
      out_channel = num_filter
      out_height = (in_height - kernel_h + pad_h) // stride_h + 1
      out_width = (in_width - kernel_w + pad_w) // stride_w + 1
      # change the layout from HWCN to NCHW
      at = a_np.transpose((3, 2, 0, 1))
      wt = w_np.transpose((3, 2, 0, 1))
      bt = np.zeros((batch, out_channel, out_height, out_width))
      # computation
      for n in range(batch):
          for f in range(out_channel):
              for c in range(in_channel):
                  if pad_h > 0 or pad_w > 0:
                      apad = np.zeros(
                          (in_height + pad_h, in_width + pad_w)
                      )
                      apad[
                          pad_top : pad_top + in_height,
                          pad_left : pad_left + in_width,
                      ] = at[n, c]
                  else:
                      apad = at[n, c]
                  out = scipy.signal.convolve2d(
                      apad, np.rot90(np.rot90(wt[f, c])), mode="valid"
                  )
                  bt[n, f] += out[::stride_h, ::stride_w]
      return bt.transpose((2, 3, 1, 0))
  
imports: import numpy
  import scipy.signal
  from tvm.topi.nn.utils import get_pad_tuple
label_desc: Use image node_id 1 for calling a global function with example usage: conv2d_hwcn_python(a_np, w_np, stride, padding) and returns: bt