class_docstr: Multi-Layer Perceptron Decoder
  
  **Parameters:**<br>
  `in_features`: int, dimension of input.<br>
  `out_features`: int, dimension of output.<br>
  `hidden_size`: int, dimension of hidden layers.<br>
  `num_layers`: int, number of hidden layers.<br>
class_docstr_tok: ["Multi-Layer","Perceptron","Decoder","*","*","Parameters",":","*","*","<","br",">","`","in_features","`",":","int",",","dimension","of","input.","<","br",">","`","out_features","`",":","int",",","dimension","of","output.","<","br",">","`","hidden_size","`",":","int",",","dimension","of","hidden","layers.","<","br",">","`","num_layers","`",":","int",",","number","of","hidden","layers.","<","br",">"]
code: def __init__(
      self, in_features, out_features, hidden_size, hidden_layers
  ):
      super().__init__()
  
      if hidden_layers == 0:
          # Input layer
          layers = [
              nn.Linear(
                  in_features=in_features, out_features=out_features
              )
          ]
      else:
          # Input layer
          layers = [
              nn.Linear(
                  in_features=in_features, out_features=hidden_size
              ),
              nn.ReLU(),
          ]
          # Hidden layers
          for i in range(hidden_layers - 2):
              layers += [
                  nn.Linear(
                      in_features=hidden_size, out_features=hidden_size
                  ),
                  nn.ReLU(),
              ]
          # Output layer
          layers += [
              nn.Linear(
                  in_features=hidden_size, out_features=out_features
              )
          ]
  
      # Store in layers as ModuleList
      self.layers = nn.Sequential(*layers)
  
imports: import numpy
  import torch
  import torch.nn
  import logging
  import warnings
  from typing import Optional
  from ..common._base_windows import BaseWindows
  from ..losses.pytorch import DistributionLoss, MQLoss
label_desc: Use image node_id 1 to create a new Decoder object from inherited base classes: nn with example: obj = Decoder(in_features, out_features, hidden_size, hidden_layers)