code: def conv2d_winograd_nchw(
      data,
      weight,
      strides,
      padding,
      dilation,
      out_dtype,
      pre_computed=False,
      auto_scheduler_rewritten_layout="",
      meta_schedule_original_shape=None,
  ):
      """Conv2D Winograd in NCHW layout.
      This is a clean version to be used by the auto-scheduler for both CPU and GPU.
  
      Parameters
      ----------
      data : tvm.te.Tensor
          4-D with shape [batch, in_channel, in_height, in_width]
      weight : tvm.te.Tensor
          4-D with shape [filter_height, filter_width, in_channel, num_filter]
      strides : int or a list/tuple of two ints
          stride size, or [stride_height, stride_width]
      padding : int or a list/tuple of two ints
          padding size, or [pad_height, pad_width]
      dilation: int or a list/tuple of two ints
          dilation size, or [dilation_height, dilation_width]
      out_dtype : str, optional
          Specifies the output data type.
      pre_computed: bool
          Whether the kernel is precomputed
      auto_scheduler_rewritten_layout: str = ""
          The layout after auto-scheduler's layout rewrite pass.
      meta_schedule_original_shape: Optional[List[PrimExpr]] = None
          The original shape of the input tensor.
  
      Returns
      -------
      output : tvm.te.Tensor
          4-D with shape [batch, out_height, out_width, out_channel]
      """
      tile_size = 4
      return _conv2d_winograd_nchw_impl(
          data,
          weight,
          strides,
          padding,
          dilation,
          out_dtype,
          tile_size,
          pre_computed,
          auto_scheduler_rewritten_layout,
          meta_schedule_original_shape,
      )
  
imports: from __future__ import absolute_import
  import re
  from collections import namedtuple
  from typing import Optional, Sequence, Union
  import numpy
  import tvm
  from tvm import auto_scheduler, te
  from ..utils import get_const_int, get_const_tuple, simplify, tag
  from .pad import pad
  from .utils import get_pad_tuple, get_pad_tuple_generic
  from .winograd_util import winograd_transform_matrices
label_desc: Use image node_id 20 for calling a global function with example usage: conv2d_winograd_nchw(data, weight, strides, padding, dilation, out_dtype, pre_computed, auto_scheduler_rewritten_layout, meta_schedule_original_shape) and returns: _conv2d_winograd_nchw_impl