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 __init__(
      self,
      inplanes: int,
      planes: int,
      cond_planes: int = None,
      stride: int = 1,
      downsample: Optional[Type[nn.Module]] = None,
      groups: int = 1,
      base_width: int = 64,
      dilation: int = 1,
      norm_layer: Optional[Type[nn.Module]] = None,
      stride_in_1x1: bool = False,
      compressed: bool = True,
      use_se: bool = True,
  ):
      super().__init__()
      if norm_layer is None:
          self.norm_layer = FrozenBatchNorm2d
      else:
          self.norm_layer = norm_layer
      self.cond_planes = cond_planes
      self.planes = planes
      self.inplanes = inplanes
  
      stride_1x1, stride_3x3 = (
          (stride, 1) if stride_in_1x1 else (1, stride)
      )
      self.width = int(planes * (base_width / 64.0)) * groups
  
      # Both self.conv2 and self.downsample layers downsample the input when
      # stride != 1
      self.conv1 = conv1x1(inplanes, self.width, stride_1x1)
      self.bn1 = self.norm_layer(self.width)
      self.conv2 = conv3x3(
          self.width, self.width, stride_3x3, groups, dilation
      )
      self.bn2 = self.norm_layer(self.width)
      self.conv3 = conv1x1(self.width, planes * self.expansion)
      self.bn3 = self.norm_layer(self.planes * self.expansion)
      self.relu = nn.ReLU(inplace=True)
      self.downsample = downsample
      self.se = None
  
      self.compressed = compressed
      self.use_se = use_se
  
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 1 to create a new MovieBottleneck object from inherited base classes: nn with example: obj = MovieBottleneck(inplanes, planes, cond_planes, stride, downsample, groups, base_width, dilation, norm_layer, stride_in_1x1, compressed, use_se)