code: def scale_shift_nchwc(Input, Scale, Shift):
      """Batch normalization operator in inference.
  
      Parameters
      ----------
      Input : tvm.te.Tensor
          5-D input tensor, NCHWc layout [batch, channel_chunk, height, width, channel_block]
  
      Scale : tvm.te.Tensor
          Scale tensor, 2-D of size [channel_chunk, channel_block]
  
      Shift : tvm.te.Tensor
          Shift tensor, 2-D of size [channel_chunk, channel_block]
  
      Returns
      -------
      Output : tvm.te.Tensor
          Output tensor, layout is NHWC
      """
      return te.compute(
          Input.shape,
          lambda b, cc, i, j, cb: Input[b, cc, i, j, cb] * Scale[cc, cb]
          + Shift[cc, cb],
          name="ScaleShift",
      )
  
imports: from __future__ import absolute_import
  import tvm
  from tvm import te
  from ..None import tag
label_desc: Use image node_id 3 for calling a global function with example usage: scale_shift_nchwc(Input, Scale, Shift) and returns: te