code: def forward(self, x):
      if self.use_cudnn:
          if self.thresholdTop is None:
              (
                  spatial_group_size,
                  spatial_group_rank,
                  _,
                  _,
                  _,
                  _,
              ) = self.spatial_parallel_args
              if self.explicit_nhwc:
                  N, H, W, C = list(x.shape)
              else:
                  N, C, H, W = list(x.shape)
              self.thresholdTop = torch.tensor(
                  [1 if spatial_group_rank > 0 else 0],
                  dtype=torch.int32,
                  device="cuda",
              )
              self.thresholdBottom = torch.tensor(
                  [
                      H - 2
                      if spatial_group_rank < spatial_group_size - 1
                      else H - 1
                  ],
                  dtype=torch.int32,
                  device="cuda",
              )
  
          if self.w_scale is None:
              # calculate scale/bias from registered buffers
              # TODO: make this better
              s1, b1 = self.bn1.get_scale_bias(self.explicit_nhwc)
              s2, b2 = self.bn2.get_scale_bias(self.explicit_nhwc)
              s3, b3 = self.bn3.get_scale_bias(self.explicit_nhwc)
              w_scale = [s1, s2, s3]
              w_bias = [b1, b2, b3]
              if self.downsample is not None:
                  s4, b4 = self.downsample[1].get_scale_bias(
                      self.explicit_nhwc
                  )
                  w_scale.append(s4)
                  w_bias.append(b4)
              out = spatial_bottleneck_function(
                  *self.spatial_parallel_args,
                  self.explicit_nhwc,
                  self.stride,
                  w_scale,
                  w_bias,
                  self.thresholdTop,
                  self.thresholdBottom,
                  x,
                  *self.w_conv,
              )
          else:
              out = spatial_bottleneck_function(
                  *self.spatial_parallel_args,
                  self.explicit_nhwc,
                  self.stride,
                  self.w_scale,
                  self.w_bias,
                  self.thresholdTop,
                  self.thresholdBottom,
                  x,
                  *self.w_conv,
              )
          return out
  
      if self.explicit_nhwc:
          raise RuntimeError(
              "explicit nhwc with native ops is not supported."
          )
  
      # fallback to native ops
      identity = x
  
      out = self.conv1(x)
      out = self.bn1(out)
      out = self.relu(out)
  
      out = self.conv2(out)
      out = self.bn2(out)
      out = self.relu(out)
  
      out = self.conv3(out)
      out = self.bn3(out)
  
      if self.downsample is not None:
          identity = self.downsample(x)
  
      out += identity
      out = self.relu(out)
  
      return out
  
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 3 for calling the SpatialBottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: out, out