class_docstr: `MLPDecoder` implements a fully connected network and uses ReLU as the
  activation function. The module projects an input tensor to `out_dim`.
  
  Args:
      config (Config): Configuration object of type MLPDecoder.Config.
      in_dim (int): Dimension of input Tensor passed to MLP.
      out_dim (int): Dimension of output Tensor produced by MLP. Defaults to 0.
  
  Attributes:
      mlp (type): Module that implements the MLP.
      out_dim (type): Dimension of the output of this module.
      hidden_dims (List[int]): Dimensions of the outputs of hidden layers.
docstr: Returns the MLP module that is used as a decoder.
docstr_tok: ["Returns","the","MLP","module","that","is","used","as","a","decoder","."]
code: def get_decoder(self) -> List[nn.Module]:
      """Returns the MLP module that is used as a decoder."""
      return [self.mlp]
  
file_path: pytext/pytext/models/decoders/mlp_decoder.py
imports: from typing import List, Optional
  import torch
  import torch.nn
  from pytext.config.module_config import Activation
  from pytext.optimizer import get_activation
  from pytext.utils.file_io import PathManager
  from pytext.utils.usage import log_class_usage
  from torch.serialization import default_restore_location
  from .decoder_base import DecoderBase
label_desc: Use image node_id 3 for calling the MLPDecoder obj's underlying member method code with example usage: obj.get_decoder() and returns: list