code: def _conv2d_transpose_nchw_python(
      a_np, w_np, stride, padding, output_padding
  ):
      """Transposed convolution operator in NCHW layout.
  
      Parameters
      ----------
      a_np : numpy.ndarray
          4-D with shape [batch, in_channel, in_height, in_width]
  
      w_np : numpy.ndarray
          4-D with shape [in_channel, num_filter, filter_height, filter_width]
  
      stride : int or a list/tuple of two ints
          Stride size, or [stride_height, stride_width]
  
      padding : int or str
          Padding size, or ['VALID', 'SAME']
  
      output_padding : int or a list/tuple of two ints
          Use to disambiguate the output shape.
  
      Returns
      -------
      b_np : np.ndarray
          4-D with shape [batch, out_channel, out_height, out_width]
      """
      batch, in_c, in_h, in_w = a_np.shape
      _, out_c, filter_h, filter_w = w_np.shape
      if isinstance(stride, int):
          stride_h = stride_w = stride
      else:
          stride_h, stride_w = stride
      if isinstance(output_padding, int):
          opad_h = opad_w = output_padding
      else:
          opad_h, opad_w = output_padding
      assert opad_h < stride_h and opad_w < stride_w
      # dilate stage
      dilated_a_np = tvm.topi.testing.dilate_python(
          a_np, [1, 1, stride_h, stride_w]
      )
      # padding stage
      fpad_top, fpad_left, fpad_bottom, fpad_right = get_pad_tuple(
          padding, (filter_h, filter_w)
      )
      bpad_top = filter_h - 1 - fpad_top
      bpad_bottom = filter_h - 1 - fpad_bottom + opad_h
      bpad_left = filter_w - 1 - fpad_left
      bpad_right = filter_w - 1 - fpad_right + opad_w
      padded_a_np = np.zeros(
          (
              batch,
              in_c,
              dilated_a_np.shape[2] + bpad_top + bpad_bottom,
              dilated_a_np.shape[3] + bpad_left + bpad_right,
          )
      ).astype(a_np.dtype)
      padded_a_np[
          :,
          :,
          bpad_top : dilated_a_np.shape[2] + bpad_top,
          bpad_left : dilated_a_np.shape[3] + bpad_left,
      ] = dilated_a_np
      # convolution stage
      out_h = (
          (in_h - 1) * stride_h
          - fpad_top
          - fpad_bottom
          + filter_h
          + opad_h
      )
      out_w = (
          (in_w - 1) * stride_w
          - fpad_left
          - fpad_right
          + filter_w
          + opad_w
      )
      b_np = np.zeros((batch, out_c, out_h, out_w)).astype(a_np.dtype)
      for n in range(batch):
          for f in range(out_c):
              for c in range(in_c):
                  out = scipy.signal.convolve2d(
                      padded_a_np[n, c], w_np[c, f], mode="valid"
                  )
                  b_np[n, f] += out
      return b_np
  
imports: import numpy
  import scipy
  import tvm.topi.testing
  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_transpose_nchw_python(a_np, w_np, stride, padding, output_padding) and returns: b_np