code: def _init_module(
      module,
      init_method="normal",
      init_std=0.02,
      hidden_size=768,
      num_layers=12,
  ):
      if init_method == "normal":
          std = init_std
      elif init_method == "small":
          # Transformers without Tears: Improving
          # the Normalization of Self-Attention - Nguyen, T. & Salazar, J. (2010)
          std = torch.as_tensor(2 / (5 * hidden_size)).sqrt()
      elif init_method == "megatron":
          std = torch.as_tensor(1 / (3 * hidden_size)).sqrt()
      elif init_method == "wang":
          std = 2 / num_layers / torch.as_tensor(hidden_size).sqrt()
      elif init_method == "deepnorm":
          std = torch.as_tensor(8 * num_layers).pow(
              -0.25
          )  # todo: apply this only to some layers
      elif init_method == "agd-orthogonal":
          std = init_std  # no std modification necessary, setting to default
      else:
          raise ValueError(f"Invalid init method {init_method} given.")
  
      if isinstance(module, torch.nn.Linear):
          # Slightly different from the TF version which uses truncated_normal for initialization
          # cf https://github.com/pytorch/pytorch/pull/5617
          module.weight.data.normal_(mean=0.0, std=std)
          if module.bias is not None:
              module.bias.data.zero_()
      elif isinstance(module, torch.nn.Embedding):
          module.weight.data.normal_(mean=0.0, std=std)
          if module.padding_idx is not None:
              module.weight.data[module.padding_idx].zero_()
      elif isinstance(module, torch.nn.LayerNorm):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0)
  
      if init_method == "agd-orthogonal":
          for name, p in module.named_parameters():
              if p.dim() == 1:
                  print(
                      f"WARNING: Biases are not supported. This breaks scaling of parameter {name} in theory."
                  )
              if p.dim() == 2:
                  torch.nn.init.orthogonal_(p)
                  p *= singular_value(p.shape)
              if p.dim() == 4:
                  for kx in range(p.shape[2]):
                      for ky in range(p.shape[3]):
                          torch.nn.init.orthogonal_(p[:, :, kx, ky])
                  p *= singular_value(p.shape)
  
imports: import torch
  import math
  from typing import Optional, Tuple
  from functools import partial
  from .embeddings import SinusoidalPositional, LearnablePositional, ScaledSinosoidal
  from .attention import get_attention_mechanism
label_desc: Use image node_id 4 for calling a global function with example usage: _init_module(module, init_method, init_std, hidden_size, num_layers) without return types