code: def depthwise_conv2d_python_nchw(
      input_np, filter_np, stride, padding
  ):
      """Depthwise convolution operator in NCHW layout.
  
      Parameters
      ----------
      input_np : numpy.ndarray
          4-D with shape [batch, in_channel, in_height, in_width]
  
      filter_np : numpy.ndarray
          4-D with shape [in_channel, channel_multiplier, filter_height, filter_width]
  
      stride : list / tuple of 2 ints
          [stride_height, stride_width]
  
      padding : str
          'VALID' or 'SAME'
  
      Returns
      -------
      output_np : np.ndarray
          4-D with shape [batch, out_channel, out_height, out_width]
      """
      batch, in_channel, in_height, in_width = input_np.shape
      (
          _,
          channel_multiplier,
          filter_height,
          filter_width,
      ) = filter_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, (filter_height, filter_width)
      )
      pad_h = pad_top + pad_bottom
      pad_w = pad_left + pad_right
  
      out_channel = in_channel * channel_multiplier
      out_height = (in_height - filter_height + pad_h) // stride_h + 1
      out_width = (in_width - filter_width + pad_w) // stride_w + 1
      output_np = np.zeros((batch, out_channel, out_height, out_width))
  
      for i in range(batch):
          for j in range(out_channel):
              apad = input_np[i, j // channel_multiplier, :, :]
              if pad_h or pad_w:
                  apad = np.pad(
                      apad,
                      [(pad_top, pad_bottom), (pad_left, pad_right)],
                      "constant",
                  )
  
              conv = _convolve2d(
                  apad,
                  np.rot90(
                      filter_np[
                          j // channel_multiplier,
                          j % channel_multiplier,
                          :,
                          :,
                      ],
                      k=2,
                  ),
              )
              output_np[i, j, :, :] = conv[
                  ::stride_h,
                  ::stride_w,
              ]
  
      return output_np
  
imports: import numpy
  from tvm.topi.nn.utils import get_pad_tuple
  from .common import _convolve2d
label_desc: Use image node_id 1 for calling a global function with example usage: depthwise_conv2d_python_nchw(input_np, filter_np, stride, padding) and returns: output_np