class_docstr: Masked Autoencoder model
  
  Args:
      model_config (MADEConfig): The MADE model configuration setting the main parameters of the
          model
class_docstr_tok: ["Masked","Autoencoder","model","Args",":","model_config","(","MADEConfig",")",":","The","MADE","model","configuration","setting","the","main","parameters","of","the","model"]
docstr: The input data is transformed toward the prior
  
  Args:
      inputs (torch.Tensor): An input tensor
  
  Returns:
      ModelOutput: An instance of ModelOutput containing all the relevant parameters
docstr_tok: ["The","input","data","is","transformed","toward","the","prior","Args",":","inputs","(","torch.Tensor",")",":","An","input","tensor","Returns",":","ModelOutput",":","An","instance","of","ModelOutput","containing","all","the","relevant","parameters"]
code: def forward(self, x: torch.tensor, **kwargs) -> ModelOutput:
      """The input data is transformed toward the prior
  
      Args:
          inputs (torch.Tensor): An input tensor
  
      Returns:
          ModelOutput: An instance of ModelOutput containing all the relevant parameters
      """
      net_output = self.net(x.reshape(x.shape[0], -1))
  
      mu = net_output[:, : self.input_dim]
      log_var = net_output[:, self.input_dim :]
  
      return ModelOutput(mu=mu, log_var=log_var)
  
file_path: benchmark_VAE/src/pythae/models/normalizing_flows/made/made_model.py
imports: import os
  import numpy
  import torch
  import torch.nn
  from pythae.models.base.base_utils import ModelOutput
  from ..base import BaseNF
  from ..layers import MaskedLinear
  from .made_config import MADEConfig
label_desc: Use image node_id 3 for calling the MADE obj's underlying member method code with example usage: obj.forward(x) and returns: ModelOutput