code: def unpack_HWNCnc_to_hwnc(packed_out, out_dtype):
      """Unpack conv2d_hwnc output from layout hwncnc to hwnc
  
       Parameters
      -----------
      packed_out : tvm.te.Tensor
          The output tensor of conv2d_hwnc.
  
      out_dtype : str
          The output dtype.
  
      Returns
      -------
      unpacked_out : tvm.te.Tensor
          The unpacked output tensor in hwnc layout.
      """
      H, W, N, O, wmma_m, wmma_n = get_const_tuple(packed_out.shape)
  
      idxmod = tvm.tir.indexmod
      idxdiv = tvm.tir.indexdiv
  
      oshape = (H, W, N * wmma_m, O * wmma_n)
      unpacked_out = te.compute(
          oshape,
          lambda h, w, n, o: packed_out[
              h,
              w,
              idxdiv(n, wmma_m),
              idxdiv(o, wmma_n),
              idxmod(n, wmma_m),
              idxmod(o, wmma_n),
          ].astype(out_dtype),
          name="output_unpack",
          tag=tag.INJECTIVE + ",unpack_hwncc",
      )
      return unpacked_out
  
file_path: tvm/python/tvm/topi/cuda/conv2d_hwnc_tensorcore.py
imports: import tvm
  from tvm import te
  from tvm import autotvm
  from tvm.target import Target
  from tvm.topi.cuda.injective import schedule_injective_from_existing
  from ..utils import get_const_tuple, traverse_inline, simplify, tag
  from ..nn.pad import pad
  from ..nn.utils import get_pad_tuple
  from .tensor_intrin import intrin_wmma_load_matrix_A
  from .tensor_intrin import intrin_wmma_load_matrix_W
  from .tensor_intrin import intrin_wmma_store_matrix
  from .tensor_intrin import intrin_wmma_gemm
label_desc: Use image node_id 1 for calling a global function with example usage: unpack_HWNCnc_to_hwnc(packed_out, out_dtype) and returns: unpacked_out