class_docstr: Language model embeddings.
  
  Arguments:
      config (TransformerConfig): config object with all necessary configs for TransformerBlock
      vocab_size (int): vocabulary size
      max_sequence_length (int): maximum size of sequence. This
                           is used for positional embedding
      add_position_embedding (bool): Add a position embedding.
      embedding_dropout_prob (float): dropout probability for embeddings
      num_tokentypes (int): Set to 0 without binary head, and 2 with a binary head . Defaults to 0.
class_docstr_tok: ["Language","model","embeddings",".","Arguments",":","config","(","TransformerConfig",")",":","config","object","with","all","necessary","configs","for","TransformerBlock","vocab_size","(","int",")",":","vocabulary","size","max_sequence_length","(","int",")",":","maximum","size","of","sequence",".","This","is","used","for","positional","embedding","add_position_embedding","(","bool",")",":","Add","a","position","embedding",".","embedding_dropout_prob","(","float",")",":","dropout","probability","for","embeddings","num_tokentypes","(","int",")",":","Set","to","0","without","binary","head",",","and","2","with","a","binary","head",".","Defaults","to","0","."]
docstr: Zero out all parameters in embedding.
docstr_tok: ["Zero","out","all","parameters","in","embedding","."]
code: def zero_parameters(self):
      """Zero out all parameters in embedding."""
      self.word_embeddings.weight.data.fill_(0)
      self.word_embeddings.weight.shared = True
      self.position_embeddings.weight.data.fill_(0)
      self.position_embeddings.weight.shared = True
      if self.num_tokentypes > 0:
          self.tokentype_embeddings.weight.data.fill_(0)
          self.tokentype_embeddings.weight.shared = True
  
file_path: megatron-lm/megatron/core/models/common/embeddings/language_model_embedding.py
imports: from typing import Literal, Optional
  import torch
  from torch import Tensor
  from megatron.core import tensor_parallel
  from megatron.core.transformer.module import MegatronModule
  from megatron.core.transformer.transformer_config import TransformerConfig
  from megatron.core.utils import make_sharded_tensor_for_checkpoint, make_tp_sharded_tensor_for_checkpoint
label_desc: Use image node_id 2 for calling the LanguageModelEmbedding obj's underlying member method code with example usage: obj.zero_parameters() without return types