class_docstr: fastNLP中各种Embedding的基类
class_docstr_tok: ["fastNLP\u4e2d\u5404\u79cdEmbedding\u7684\u57fa\u7c7b"]
docstr: 对embedding后的word表示进行drop。
  
  :param torch.FloatTensor words: batch_size x max_len x embed_size
  :return:
docstr_tok: ["\u5bf9embedding\u540e\u7684word\u8868\u793a\u8fdb\u884cdrop\u3002",":","param","torch.FloatTensor","words",":","batch_size","x","max_len","x","embed_size",":","return",":"]
code: def dropout(self, words):
      r"""
      对embedding后的word表示进行drop。
  
      :param torch.FloatTensor words: batch_size x max_len x embed_size
      :return:
      """
      return self.dropout_layer(words)
  
imports: from abc import abstractmethod
  from typing import Union, Tuple
  from ...envs.imports import _NEED_IMPORT_TORCH
  import numpy
  from .utils import get_embeddings
label_desc: Use image node_id 3 for calling the TokenEmbedding obj's underlying member method code with example usage: obj.dropout(words) and returns: self