code: def _conv3d_ndhwc_python(a_np, w_np, stride, padding):
      """Convolution 3D operator in NDHWC layout.
  
      Parameters
      ----------
      a_np : numpy.ndarray
          5-D with shape [batch, in_channel, in_depth, in_height, in_width]
  
      w_np : numpy.ndarray
          5-D with shape [num_filter, in_channel, filter_depth, filter_height, filter_width]
  
      stride : int or a list/tuple of three ints
          Stride size, or [stride_depth, stride_height, stride_width]
  
      padding : int or str or a list/tuple of three ints
          Padding size, or ['VALID', 'SAME'], or [pad_depth, pad_height, pad_width]
  
      Returns
      -------
      b_np : np.ndarray
          5-D with shape [batch, out_channel, out_depth, out_height, out_width]
      """
      batch, in_depth, in_height, in_width, in_channel = a_np.shape
      kernel_d, kernel_h, kernel_w, _, num_filter = w_np.shape
      if isinstance(stride, int):
          stride_d = stride_h = stride_w = stride
      else:
          stride_d, stride_h, stride_w = stride
  
      (
          pad_front,
          pad_top,
          pad_left,
          pad_back,
          pad_bottom,
          pad_right,
      ) = get_pad_tuple3d(padding, (kernel_d, kernel_h, kernel_w))
      pad_d = pad_front + pad_back
      pad_h = pad_top + pad_bottom
      pad_w = pad_left + pad_right
      # compute the output shape
      out_channel = num_filter
      out_depth = (in_depth - kernel_d + pad_d) // stride_d + 1
      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 NHWC to NCHW
      at = a_np.transpose((0, 4, 1, 2, 3))
      wt = w_np.transpose((4, 3, 0, 1, 2))
      bt = np.zeros(
          (batch, out_channel, out_depth, out_height, out_width),
          dtype=a_np.dtype,
      )
      # computation
      for n in range(batch):
          for f in range(out_channel):
              for c in range(in_channel):
                  if pad_d > 0 or pad_h > 0 or pad_w > 0:
                      apad = np.zeros(
                          (
                              in_depth + pad_d,
                              in_height + pad_h,
                              in_width + pad_w,
                          ),
                          dtype=a_np.dtype,
                      )
                      apad[
                          pad_front : pad_front + in_depth,
                          pad_top : pad_top + in_height,
                          pad_left : pad_left + in_width,
                      ] = at[n, c]
                  else:
                      apad = at[n, c]
                  out = scipy.signal.convolve(
                      apad, np.flip(wt[f, c]), mode="valid"
                  )
                  bt[n, f] += out[::stride_d, ::stride_h, ::stride_w]
      return bt.transpose((0, 2, 3, 4, 1))
  
imports: import numpy
  import scipy.signal
  from tvm.topi.nn.utils import get_pad_tuple3d
label_desc: Use image node_id 1 for calling a global function with example usage: _conv3d_ndhwc_python(a_np, w_np, stride, padding) and returns: bt