class_docstr: Language model embeddings.
  
  Arguments:
      hidden_size: hidden size
      vocab_size: vocabulary size
      max_sequence_length: maximum size of sequence. This
                           is used for positional embedding
      embedding_dropout_prob: dropout probability for embeddings
      init_method: weight initialization method
      num_tokentypes: size of the token-type embeddings. 0 value
                      will ignore this embedding
class_docstr_tok: ["Language","model","embeddings",".","Arguments",":","hidden_size",":","hidden","size","vocab_size",":","vocabulary","size","max_sequence_length",":","maximum","size","of","sequence",".","This","is","used","for","positional","embedding","embedding_dropout_prob",":","dropout","probability","for","embeddings","init_method",":","weight","initialization","method","num_tokentypes",":","size","of","the","token-type","embeddings",".","0","value","will","ignore","this","embedding"]
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
  
imports: import torch
  import torch.nn.init
  from torch import Tensor
  from torch import nn
  from torch.nn import functional
  from torch.nn.parameter import Parameter
  from colossalai.legacy.context import ParallelMode, seed
  from colossalai.legacy.core import global_context
  from colossalai.legacy.nn.layer.base_layer import ParallelLayer
  from colossalai.legacy.nn.layer.parallel_1d._utils import gather_forward_split_backward, reduce_grad, reduce_input
  from colossalai.legacy.nn.layer.parallel_1d.layers import Linear1D_Row
  from colossalai.legacy.nn.layer.utils import divide
  from colossalai.legacy.registry import LAYERS, LOSSES
  from colossalai.utils import get_current_device
label_desc: Use image node_id 2 for calling the VocabParallelEmbedding obj's underlying member method code with example usage: obj.zero_parameters() without return types