class_docstr: Pretraining machinery using SCRIPT from Nijkamp et al., 2021. Always running sparse prediction.
class_docstr_tok: ["Pretraining","machinery","using","SCRIPT","from","Nijkamp","et","al.",",","2021",".","Always","running","sparse","prediction","."]
code: def _init_weights(self, module=None):
      modules = self.modules() if module is None else [module]
      for module in modules:
          _init_module(
              module,
              self.cfg.init.type,
              self.cfg.init.std,
              self.cfg.hidden_size,
              self.cfg.num_transformer_layers,
          )
  
imports: import torch
  from transformers import PretrainedConfig, PreTrainedModel
  from transformers import AutoConfig, AutoModel, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification
  from typing import Optional
  from omegaconf import OmegaConf
  from .fff import FFF
  from .components import _get_norm_fn, _get_nonlin_fn, EmbeddingComponent, PoolingComponent, PredictionHeadComponent, GLU, get_extended_attention_mask, _init_module
  from .attention import get_attention_mechanism
label_desc: Use image node_id 2 for calling the ScriptableLMForSCRIPTTraining obj's underlying member method code with example usage: obj._init_weights(module) without return types