code: def forward(self, x):
      residual = x
  
      out = self.conv1(x)
      out = self.bn1(out)
      out = self.relu(out)
  
      # out = self.conv2(out)
      if not self.with_dcn:
          out = self.conv2(out)
      elif self.with_modulated_dcn:
          offset_mask = self.conv2_offset(out)
          offset = offset_mask[:, :18, :, :]
          mask = offset_mask[:, -9:, :, :].sigmoid()
          out = self.conv2(out, offset, mask)
      else:
          offset = self.conv2_offset(out)
          out = self.conv2(out, offset)
      out = self.bn2(out)
      out = self.relu(out)
  
      out = self.conv3(out)
      out = self.bn3(out)
  
      if self.downsample is not None:
          residual = self.downsample(x)
  
      out += residual
      out = self.relu(out)
  
      return out
  
imports: import torch.nn
  import math
  import torch.utils.model_zoo
label_desc: Use image node_id 2 for calling the Bottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: out