docstr: Forward pass of the neural network.
  
  Parameters
  ----------
  x : torch.tensor
      The input tensor.
docstr_tok: ["Forward","pass","of","the","neural","network",".","Parameters","--","--","--","--","--","x",":","torch.tensor","The","input","tensor","."]
code: def forward(self, x: torch.tensor):
      """Forward pass of the neural network.
  
      Parameters
      ----------
      x : torch.tensor
          The input tensor.
      """
      x = x.to(self.device)
      for layer in self.layers:
          x = layer(x)
      return x
  
file_path: aequitas/src/aequitas/fairflow/methods/base_estimator/neural_network.py
imports: from typing import Optional
  import pandas
  import torch
  from torch import nn, optim
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from ...utils import create_logger, import_object
  from .base_estimator import BaseEstimator
label_desc: Use image node_id 4 for calling the TorchNeuralNetwork obj's underlying member method code with example usage: obj.forward(x) and returns: x