class_docstr: Ganomaly Model.
  
  Args:
      input_size (tuple[int, int]): Input dimension.
      num_input_channels (int): Number of input channels.
      n_features (int): Number of features layers in the CNNs.
      latent_vec_size (int): Size of autoencoder latent vector.
      extra_layers (int, optional): Number of extra layers for encoder/decoder. Defaults to 0.
      add_final_conv_layer (bool, optional): Add convolution layer at the end. Defaults to True.
class_docstr_tok: ["Ganomaly","Model",".","Args",":","input_size","(","tuple","[","int",",","int","]",")",":","Input","dimension",".","num_input_channels","(","int",")",":","Number","of","input","channels",".","n_features","(","int",")",":","Number","of","features","layers","in","the","CNNs",".","latent_vec_size","(","int",")",":","Size","of","autoencoder","latent","vector",".","extra_layers","(","int",",","optional",")",":","Number","of","extra","layers","for","encoder\/decoder",".","Defaults","to","0.","add_final_conv_layer","(","bool",",","optional",")",":","Add","convolution","layer","at","the","end",".","Defaults","to","True","."]
docstr: Initialize DCGAN weights.
  
  Args:
      module (nn.Module): [description]
docstr_tok: ["Initialize","DCGAN","weights",".","Args",":","module","(","nn.Module",")",":","[","description","]"]
code: def weights_init(module: nn.Module) -> None:
      """Initialize DCGAN weights.
  
      Args:
          module (nn.Module): [description]
      """
      classname = module.__class__.__name__
      if classname.find("Conv") != -1:
          nn.init.normal_(module.weight.data, 0.0, 0.02)
      elif classname.find("BatchNorm") != -1:
          nn.init.normal_(module.weight.data, 1.0, 0.02)
          nn.init.constant_(module.bias.data, 0)
  
imports: from __future__ import annotations
  import math
  import torch
  from torch import Tensor, nn
  from anomalib.data.utils.image import pad_nextpow2
label_desc: Use image node_id 2 for calling the GanomalyModel obj's underlying member method code with example usage: obj.weights_init(module) without return types