code: def __init__(
      self, channels: List[int], strides: List[int], layers: List[int]
  ):
      super().__init__()
      if len(channels) != len(strides):
          raise ValueError(
              "Number of channels must be equal to number of strides"
          )
  
      self._layers = list(layers)
      self.output_spec = FeatureMapsSpecification(
          channels=_take_ints(channels, layers),
          strides=_take_ints(strides, layers),
      )
  
imports: import math
  import warnings
  from typing import List, Union, Tuple, Iterable, Any
  import torch
  from torch import nn, Tensor
  from pytorch_toolbelt.utils.support import pytorch_toolbelt_deprecated
  from pytorch_toolbelt.modules.interfaces import HasOutputFeaturesSpecification, FeatureMapsSpecification, AbstractEncoder
label_desc: Use image node_id 1 to create a new EncoderModule object from inherited base classes: AbstractEncoder with example: obj = EncoderModule(channels, strides, layers)