docstr: Initializes the weights of the neural network.
  
  Parameters
  ----------
  module : nn.Module
      The neural network module.
docstr_tok: ["Initializes","the","weights","of","the","neural","network",".","Parameters","--","--","--","--","--","module",":","nn.Module","The","neural","network","module","."]
code: def _init_weights(self, module: nn.Module) -> None:
      """Initializes the weights of the neural network.
  
      Parameters
      ----------
      module : nn.Module
          The neural network module.
      """
      torch.manual_seed(self.seed)
      if isinstance(module, nn.Embedding):
          module.weight.data.normal_(mean=0.0, std=1.0)
          if module.padding_idx is not None:
              module.weight.data[module.padding_idx].zero_()
          elif isinstance(module, nn.BatchNorm1d):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0)
          elif isinstance(module, nn.Linear):
              module.weight.data.normal_(mean=0.0, std=1.0)
              module.bias.data.zero_()
  
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 3 for calling the TorchNeuralNetwork obj's underlying member method code with example usage: obj._init_weights(module) without return types