code: def __init__(
      self,
      num_features: int,
      num_cond_features: int,
      compressed: bool = True,
  ):
      super().__init__()
      self.linear = nn.Linear(num_cond_features, num_features)
      self.conv = (
          nn.Conv2d(num_features, 256, kernel_size=1)
          if compressed
          else nn.Conv2d(num_features, num_features, kernel_size=1)
      )
  
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 Modulation object from inherited base classes: nn with example: obj = Modulation(num_features, num_cond_features, compressed)