code: def forward(
      ctx,
      x,
      s,
      b,
      rm,
      riv,
      mini_m,
      mini_riv,
      ret_cta,
      mom,
      epsilon,
      fuse_relu,
      is_train,
      bn_group,
      my_data,
      pair_data,
      magic,
      pair_data2,
      pair_data3,
      fwd_occup,
      fwd_grid_x,
      bwd_occup,
      bwd_grid_x,
      multi_stream,
  ):
      if is_train:
          ctx.save_for_backward(x, s, b, rm, riv, mini_m, mini_riv)
          ctx.epsilon = epsilon
          ctx.momentum = mom
          ctx.ret_cta = ret_cta
          ctx.fuse_relu = fuse_relu
          ctx.my_data = my_data
          ctx.pair_data = pair_data
          ctx.magic = magic
          ctx.pair_data2 = pair_data2
          ctx.pair_data3 = pair_data3
          ctx.bn_group = bn_group
          ctx.bwd_occup = bwd_occup
          ctx.bwd_grid_x = bwd_grid_x
          ctx.multi_stream = multi_stream
  
          res = bnp.bn_fwd_nhwc(
              x,
              s,
              b,
              rm,
              riv,
              mini_m,
              mini_riv,
              ret_cta,
              mom,
              epsilon,
              fuse_relu,
              my_data,
              pair_data,
              pair_data2,
              pair_data3,
              bn_group,
              magic,
              fwd_occup,
              fwd_grid_x,
              multi_stream,
          )
          return res
      else:
          return bnp.bn_fwd_eval_nhwc(
              x,
              s,
              b,
              rm,
              riv,
              ret_cta,
              bn_group,
              mom,
              epsilon,
              fuse_relu,
          )
  
file_path: apex/apex/contrib/groupbn/batch_norm.py
imports: import torch
  import numpy
  from torch.nn.modules.batchnorm import _BatchNorm
  import bnp
label_desc: Use image node_id 1 for calling the bn_NHWC_impl obj's underlying member method code with example usage: obj.forward(ctx, x, s, b, rm, riv, mini_m, mini_riv, ret_cta, mom, epsilon, fuse_relu, is_train, bn_group, my_data, pair_data, magic, pair_data2, pair_data3, fwd_occup, fwd_grid_x, bwd_occup, bwd_grid_x, multi_stream) and returns: res, bnp