code: def conv3d_transpose_ncdhw_python(
      a_np, w_np, stride, padding, output_padding, groups=1
  ):
      """Transposed 3d convolution operator in NCDHW 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 [in_channel, num_filter, filter_depth, filter_height, filter_width]
  
      stride : int or a list/tuple of two ints
          Stride size, or [stride_depth, stride_height, stride_width]
  
      padding : int or str
          Padding size
  
      output_padding : int or list/tuple of three ints
          Used to disambiguate output shape.
  
      groups : int
          Number of groups
  
      Returns
      -------
      b_np : np.ndarray
          5-D with shape [batch, out_channel, out_depth, 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 = [
          _conv3d_transpose_ncdhw_python(
              a_slice, w_slice, stride, padding, output_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 tvm.topi.testing
  from tvm.topi.nn.utils import get_pad_tuple3d
label_desc: Use image node_id 2 for calling a global function with example usage: conv3d_transpose_ncdhw_python(a_np, w_np, stride, padding, output_padding, groups) and returns: b_np