class_docstr: the full GPT language model, with a context size of block_size
class_docstr_tok: ["the","full","GPT","language","model",",","with","a","context","size","of","block_size"]
code: def _init_weights(self, module):
      if isinstance(module, (nn.Linear, nn.Embedding)):
          module.weight.data.normal_(mean=0.0, std=0.02)
          if isinstance(module, nn.Linear) and module.bias is not None:
              module.bias.data.zero_()
      elif isinstance(module, nn.LayerNorm):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0)
  
      # Reset the token counter
      self._tokens_seen = 0
  
imports: import math
  import os
  import pytorch_lightning
  import torch
  import torch.nn
  from pytorch_lightning import Trainer, seed_everything
  from pytorch_lightning.utilities import rank_zero_info
  from torch.nn import functional
  from torch.utils.data import DataLoader, Dataset, RandomSampler
  from xformers.factory.model_factory import xFormer, xFormerConfig
label_desc: Use image node_id 2 for calling the GPT obj's underlying member method code with example usage: obj._init_weights(module) without return types