code: def conv2d_transpose_nchw(
      cfg, Input, Filter, strides, padding, out_dtype, output_padding
  ):
      """Transposed 2D convolution nchw forward operator.
  
      Parameters
      ----------
      Input : tvm.te.Tensor
          4-D with shape [batch, in_channel, in_height, in_width]
  
      Filter : tvm.te.Tensor
          4-D with shape [in_channel, num_filter, filter_height, filter_width]
  
      strides : tuple of two ints
          The spatial stride along height and width
  
      padding : int or str
          Padding size, or ['VALID', 'SAME']
  
      out_dtype: str
          The output data type. This is used for mixed precision.
  
      output_padding : tuple of int
          Used to get the right output shape in gradients
  
      Returns
      -------
      Output : tvm.te.Tensor
          4-D with shape [batch, out_channel, out_height, out_width]
      """
      return _decl_spatial_pack(
          cfg,
          Input,
          Filter,
          strides,
          padding,
          "NCHW",
          out_dtype,
          2,
          output_padding,
      )
  
file_path: tvm/python/tvm/topi/arm_cpu/conv2d_transpose.py
imports: from __future__ import absolute_import
  import tvm
  from tvm import te
  from tvm import autotvm
  from ..nn import dilate, pad, get_pad_tuple
  from ..utils import get_const_tuple, traverse_inline
  from .conv2d_spatial_pack import schedule_conv2d_spatial_pack_nchw
label_desc: Use image node_id 1 for calling a global function with example usage: conv2d_transpose_nchw(cfg, Input, Filter, strides, padding, out_dtype, output_padding) and returns: _decl_spatial_pack