code: def forward(
      ctx,
      spatial_group_size,
      spatial_group_rank,
      spatial_communicator,
      spatial_halo_exchanger,
      spatial_method,
      use_delay_kernel,
      explicit_nhwc,
      stride_1x1,
      scale,
      bias,
      thresholdTop,
      thresholdBottom,
      x,
      *conv,
  ):
      if spatial_group_size > 1:
          stream1 = spatial_halo_exchanger.stream1
          stream2 = spatial_halo_exchanger.stream2
          stream3 = spatial_halo_exchanger.stream3
  
      # TODO: clean up order of tensors
      args = [x, *conv[0:3], *scale[0:3], *bias[0:3]]
      ctx.downsample = len(conv) > 3
      if ctx.downsample:
          args.append(conv[3])
          args.append(scale[3])
          args.append(bias[3])
  
      # weight buffers are always in explicit_nhwc while shape can be explicit_nhwc or channels_last
      # here we pass in flag and let c++ handle it
      # alternatively, we can put all sizes into a fixed format and pass it in
      outputs = fast_bottleneck.forward_init(
          explicit_nhwc, stride_1x1, args
      )
      fast_bottleneck.forward_out1(
          explicit_nhwc, stride_1x1, args, outputs
      )
  
      if spatial_group_size > 1:
          out1 = outputs[0]
          if explicit_nhwc:
              N, Hs, W, C = list(out1.shape)
              memory_format = torch.contiguous_format
              out1_pad = torch.empty(
                  [N, Hs + 2, W, C], dtype=out1.dtype, device="cuda"
              )
          else:
              N, C, Hs, W = list(out1.shape)
              memory_format = (
                  torch.channels_last
                  if out1.is_contiguous(
                      memory_format=torch.channels_last
                  )
                  else torch.contiguous_format
              )
              out1_pad = torch.empty(
                  [N, C, Hs + 2, W],
                  dtype=out1.dtype,
                  device="cuda",
                  memory_format=memory_format,
              )
          stream1.wait_stream(torch.cuda.current_stream())
          if spatial_method != 2:
              stream3.wait_stream(torch.cuda.current_stream())
          with torch.cuda.stream(stream1):
              if explicit_nhwc:
                  top_out1_halo = out1_pad[:, :1, :, :]
                  btm_out1_halo = out1_pad[:, Hs + 1 : Hs + 2, :, :]
                  spatial_halo_exchanger.left_right_halo_exchange(
                      out1[:, :1, :, :],
                      out1[:, Hs - 1 :, :, :],
                      top_out1_halo,
                      btm_out1_halo,
                  )
              else:
                  top_out1_halo = out1_pad[:, :, :1, :]
                  btm_out1_halo = out1_pad[:, :, Hs + 1 : Hs + 2, :]
                  spatial_halo_exchanger.left_right_halo_exchange(
                      out1[:, :, :1, :],
                      out1[:, :, Hs - 1 :, :],
                      top_out1_halo,
                      btm_out1_halo,
                  )
          if spatial_method == 1:
              # overlap mid convolution with halo transfer
              if spatial_group_rank < spatial_group_size - 1:
                  stream2.wait_stream(stream1)
                  with torch.cuda.stream(stream2):
                      if explicit_nhwc:
                          btm_fat_halo = torch.empty(
                              (N, 3, W, C),
                              dtype=out1.dtype,
                              device=out1.device,
                          )
                          btm_fat_halo[:, 0:2, :, :].copy_(
                              out1[:, Hs - 2 :, :, :]
                          )
                          btm_fat_halo[:, 2:, :, :].copy_(btm_out1_halo)
                      else:
                          btm_fat_halo = torch.empty(
                              (N, C, 3, W),
                              dtype=out1.dtype,
                              device=out1.device,
                          )
                          btm_fat_halo[:, :, 0:2, :].copy_(
                              out1[:, :, Hs - 2 :, :]
                          )
                          btm_fat_halo[:, :, 2:, :].copy_(btm_out1_halo)
                      btm_out2 = fast_bottleneck.forward_out2_halo(
                          explicit_nhwc, btm_fat_halo, args
                      )
              if spatial_group_rank > 0:
                  with torch.cuda.stream(stream1):
                      if explicit_nhwc:
                          top_fat_halo = torch.empty(
                              (N, 3, W, C),
                              dtype=out1.dtype,
                              device=out1.device,
                          )
                          top_fat_halo[:, :1, :, :].copy_(top_out1_halo)
                          top_fat_halo[:, 1:3, :, :].copy_(
                              out1[:, :2, :, :]
                          )
                      else:
                          top_fat_halo = torch.empty(
                              (N, C, 3, W),
                              dtype=out1.dtype,
                              device=out1.device,
                          )
                          top_fat_halo[:, :, :1, :].copy_(top_out1_halo)
                          top_fat_halo[:, :, 1:3, :].copy_(
                              out1[:, :, :2, :]
                          )
                      top_out2 = fast_bottleneck.forward_out2_halo(
                          explicit_nhwc, top_fat_halo, args
                      )
              if use_delay_kernel:
                  inc.add_delay(10)
          elif spatial_method != 2 and spatial_method != 3:
              assert False, "spatial_method must be 1, 2 or 3"
  
      if spatial_group_size <= 1:
          fast_bottleneck.forward_out2(
              explicit_nhwc, stride_1x1, args, outputs
          )
      elif spatial_method == 1:
          fast_bottleneck.forward_out2(
              explicit_nhwc, stride_1x1, args, outputs
          )
          with torch.cuda.stream(stream3):
              if explicit_nhwc:
                  out1_pad[:, 1 : Hs + 1, :, :].copy_(out1)
              else:
                  out1_pad[:, :, 1 : Hs + 1, :].copy_(out1)
      elif spatial_method == 2:
          # wait for halo transfer to finish before doing a full convolution of padded x
          if explicit_nhwc:
              out1_pad[:, 1 : Hs + 1, :, :].copy_(out1)
          else:
              out1_pad[:, :, 1 : Hs + 1, :].copy_(out1)
          torch.cuda.current_stream().wait_stream(stream1)
          fast_bottleneck.forward_out2_pad(
              explicit_nhwc, stride_1x1, args, outputs, out1_pad
          )
      elif spatial_method == 3:
          fast_bottleneck.forward_out2_mask(
              explicit_nhwc,
              stride_1x1,
              args,
              outputs,
              thresholdTop,
              thresholdBottom,
          )
          with torch.cuda.stream(stream3):
              if explicit_nhwc:
                  out1_pad[:, 1 : Hs + 1, :, :].copy_(out1)
              else:
                  out1_pad[:, :, 1 : Hs + 1, :].copy_(out1)
  
      # compute halo cells for outputs[1] (out2)
      if spatial_group_size > 1:
          out2 = outputs[1]
          if explicit_nhwc:
              top_out2_halo = out2[:, :1, :, :]
              btm_out2_halo = out2[:, Hs - 1 :, :, :]
          else:
              top_out2_halo = out2[:, :, :1, :]
              btm_out2_halo = out2[:, :, Hs - 1 :, :]
          if spatial_method == 1:
              if spatial_group_rank > 0:
                  torch.cuda.current_stream().wait_stream(stream1)
                  top_out2_halo.copy_(top_out2)
              if spatial_group_rank < spatial_group_size - 1:
                  torch.cuda.current_stream().wait_stream(stream2)
                  btm_out2_halo.copy_(btm_out2)
          elif spatial_method == 3:
              # Note
              # out2 halo correction cannot overlap with anything since it has
              # to wait for out2_mask to finish, but itself has to finish before
              # the first kernel of _forward_rest can launch.
              # At least we can overlap the two halo correction kernels.
              if spatial_group_rank < spatial_group_size - 1:
                  stream2.wait_stream(
                      stream1
                  )  # wait for halo transfers to finish
                  stream2.wait_stream(
                      torch.cuda.current_stream()
                  )  # wait for *_out2_mask to finish
                  with torch.cuda.stream(stream2):
                      w1by3 = args[2][:, 2:3, :, :].clone()
                      btm_out1_halo = btm_out1_halo.clone()
                      btm_out2 = fast_bottleneck.forward_out2_halo_corr(
                          explicit_nhwc,
                          btm_out1_halo,
                          args,
                          w1by3,
                          btm_out2_halo.clone(),
                      )
                      btm_out2_halo.copy_(btm_out2)
              if spatial_group_rank > 0:
                  stream1.wait_stream(
                      torch.cuda.current_stream()
                  )  # wait for *_out2_mask to finish
                  with torch.cuda.stream(stream1):
                      w1by3 = args[2][:, :1, :, :].clone()
                      top_out1_halo = top_out1_halo.clone()
                      top_out2 = fast_bottleneck.forward_out2_halo_corr(
                          explicit_nhwc,
                          top_out1_halo,
                          args,
                          w1by3,
                          top_out2_halo.clone(),
                      )
                      top_out2_halo.copy_(top_out2)
              if spatial_group_rank < spatial_group_size - 1:
                  torch.cuda.current_stream().wait_stream(stream2)
              if spatial_group_rank > 0:
                  torch.cuda.current_stream().wait_stream(stream1)
  
      fast_bottleneck.forward_rest(
          explicit_nhwc, stride_1x1, args, outputs
      )
      # save halos for backward pass
      if spatial_group_size > 1:
          if spatial_method != 2:
              # make sure copy of mid-section of out1 into out1_pad is done before exiting
              torch.cuda.current_stream().wait_stream(stream3)
          ctx.save_for_backward(
              *(
                  args
                  + outputs
                  + [
                      out1_pad,
                  ]
              )
          )
      else:
          ctx.save_for_backward(*(args + outputs))
      # save relu outputs for drelu
      ctx.explicit_nhwc = explicit_nhwc
      ctx.stride_1x1 = stride_1x1
      ctx.spatial_group_size = spatial_group_size
      if spatial_group_size > 1:
          ctx.spatial_group_rank = spatial_group_rank
          ctx.spatial_halo_exchanger = spatial_halo_exchanger
          ctx.spatial_method = spatial_method
          ctx.use_delay_kernel = use_delay_kernel
          ctx.thresholdTop = thresholdTop
          ctx.thresholdBottom = thresholdBottom
          ctx.stream1 = stream1
          ctx.stream2 = stream2
          ctx.stream3 = stream3
      return outputs[2]
  
imports: import functools
  import torch
  import torch.distributed
  from torch import nn
  from apex import check_cudnn_version_and_warn
  import fast_bottleneck
  import nccl_p2p_cuda
label_desc: Use image node_id 1 for calling the SpatialBottleneckFunction obj's underlying member method code with example usage: obj.forward(ctx, spatial_group_size, spatial_group_rank, spatial_communicator, spatial_halo_exchanger, spatial_method, use_delay_kernel, explicit_nhwc, stride_1x1, scale, bias, thresholdTop, thresholdBottom, x) and returns: outputs