class_docstr: Standard ResNet bottleneck with MoVie modulation in
  https://arxiv.org/abs/2004.11883
  The code is inspired from
  https://pytorch.org/docs/stable/_modules/torchvision/models/resnet.html
class_docstr_tok: ["Standard","ResNet","bottleneck","with","MoVie","modulation","in","https",":","\/\/arxiv.org\/abs\/2004.11883","The","code","is","inspired","from","https",":","\/\/pytorch.org\/docs\/stable\/_modules\/torchvision\/models\/resnet.html"]
code: def forward(
      self, x: torch.Tensor, cond: Optional[torch.Tensor] = None
  ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
      identity = x
  
      if self.cond_planes and self.compressed:
          x = self.conv1(x) + self.cond(x, cond)
      elif self.cond_planes and not self.compressed:
          x += self.cond(x, cond)
          x = self.conv1(x)
      else:
          x = self.conv1(x)
  
      out = self.bn1(x)
      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:
          shortcut = self.downsample(identity)
      else:
          shortcut = identity
  
      if self.se:
          out = self.se(out)
  
      out += shortcut
      out = self.relu(out)
  
      return out, cond
  
imports: from collections import OrderedDict
  from typing import Optional, Tuple, Type
  import torch
  import torch.nn
  from torchvision.models.resnet import Bottleneck, conv1x1, conv3x3
  from torchvision.ops.misc import FrozenBatchNorm2d
label_desc: Use image node_id 3 for calling the MovieBottleneck obj's underlying member method code with example usage: obj.forward(x, cond) and returns: out,  cond