class_docstr: CNN-LSTM embedder (based on Ma and Hovy. 2016)
  
  Args:
      word_vocab_size (int): word vocabulary size
      num_labels (int): number of labels (classifier)
      word_embedding_dims (int, optional): word embedding dims
      char_embedding_dims (int, optional): character embedding dims
      cnn_kernel_size (int, optional): character CNN kernel size
      cnn_num_filters (int, optional): character CNN number of filters
      lstm_hidden_size (int, optional): LSTM embedder hidden size
      lstm_layers (int, optional): num of LSTM layers
      bidir (bool, optional): apply bi-directional LSTM
      dropout (float, optional): dropout rate
      padding_idx (int, optinal): padding number for embedding layers
class_docstr_tok: ["CNN-LSTM","embedder","(","based","on","Ma","and","Hovy",".","2016",")","Args",":","word_vocab_size","(","int",")",":","word","vocabulary","size","num_labels","(","int",")",":","number","of","labels","(","classifier",")","word_embedding_dims","(","int",",","optional",")",":","word","embedding","dims","char_embedding_dims","(","int",",","optional",")",":","character","embedding","dims","cnn_kernel_size","(","int",",","optional",")",":","character","CNN","kernel","size","cnn_num_filters","(","int",",","optional",")",":","character","CNN","number","of","filters","lstm_hidden_size","(","int",",","optional",")",":","LSTM","embedder","hidden","size","lstm_layers","(","int",",","optional",")",":","num","of","LSTM","layers","bidir","(","bool",",","optional",")",":","apply","bi-directional","LSTM","dropout","(","float",",","optional",")",":","dropout","rate","padding_idx","(","int",",","optinal",")",":","padding","number","for","embedding","layers"]
docstr: Load pre-defined word embeddings
  
  Args:
      embeddings (torch.tensor): word embedding tensor
docstr_tok: ["Load","pre-defined","word","embeddings","Args",":","embeddings","(","torch.tensor",")",":","word","embedding","tensor"]
code: def load_embeddings(self, embeddings):
      """
      Load pre-defined word embeddings
  
      Args:
          embeddings (torch.tensor): word embedding tensor
      """
      self.word_embeddings = nn.Embedding.from_pretrained(
          embeddings, freeze=False, padding_idx=self.padding_idx
      )
  
file_path: nlp-architect/nlp_architect/nn/torch/modules/embedders.py
imports: import os
  from typing import List
  import torch
  from torch import nn
  import torch.nn.functional
  from nlp_architect.utils.io import load_json_file
  from nlp_architect.utils.text import n_letters
label_desc: Use image node_id 2 for calling the CNNLSTM obj's underlying member method code with example usage: obj.load_embeddings(embeddings) without return types