code: def conv2d_nchw_python(a_np, w_np, stride, padding, groups=1):
      """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 [num_filter, in_channel // groups, filter_height, filter_width]
  
      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
  
      groups : int
          Number of groups
  
      Returns
      -------
      b_np : np.ndarray
          4-D with shape [batch, out_channel, out_height, out_width]
      """
      a_slices = np.array_split(a_np, groups, axis=1)
      w_slices = np.array_split(w_np, groups, axis=0)
      b_slices = [
          _conv2d_nchw_python(a_slice, w_slice, stride, padding)
          for a_slice, w_slice in zip(a_slices, w_slices)
      ]
      b_np = np.concatenate(b_slices, axis=1)
      return b_np
  
imports: import numpy
  import scipy
  from tvm.topi.nn.utils import get_pad_tuple
label_desc: Use image node_id 3 for calling a global function with example usage: conv2d_nchw_python(a_np, w_np, stride, padding, groups) and returns: b_np