code: def __init__(
      self,
      input_layer: Optional[nn.Module],
      mlp: Optional[nn.Module] = None,
      input_dim: Optional[int] = None,
      enc_dim: Optional[int] = None,
      step_dim: Optional[int] = None,
  ) -> None:
      super().__init__()
      self.input_layer = input_layer
      if isinstance(mlp, nn.Module):
          self.mlp = mlp
      elif isinstance(enc_dim, int) and isinstance(step_dim, int):
          self.mlp = nn.Sequential(
              nn.Flatten(),
              nn.Linear(input_dim, enc_dim + 2 * step_dim),
              nn.ReLU(),
              nn.Linear(enc_dim + 2 * step_dim, enc_dim + step_dim),
              nn.ReLU(),
              nn.Linear(enc_dim + step_dim, enc_dim),
          )
      else:
          raise ValueError(
              "Need to provide either `enc_dim` and `step_dim` or a "
              "nn.Module `mlp`"
          )
  
file_path: alibi-detect/alibi_detect/cd/pytorch/preprocess.py
imports: from typing import Callable, Dict, Optional, Type, Union
  import numpy
  import torch
  import torch.nn
  from alibi_detect.utils.pytorch.prediction import predict_batch, predict_batch_transformer
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 1 to create a new _Encoder object from inherited base classes: nn with example: obj = _Encoder(input_layer, mlp, input_dim, enc_dim, step_dim)