class_docstr: A Conv network to generate word embedding from character embeddings, from
  Character-Aware Neural Language Models, https://arxiv.org/abs/1508.06615.
  
  Components include convolutional filters, pooling, and
  optional highway network. We also have the ability to use pretrained ELMo
  which corresponds to the byte embeddings, CNN weights and the highway layer.
class_docstr_tok: ["A","Conv","network","to","generate","word","embedding","from","character","embeddings",",","from","Character-Aware","Neural","Language","Models",",","https",":","\/\/arxiv.org\/abs\/1508.06615",".","Components","include","convolutional","filters",",","pooling",",","and","optional","highway","network",".","We","also","have","the","ability","to","use","pretrained","ELMo","which","corresponds","to","the","byte","embeddings",",","CNN","weights","and","the","highway","layer","."]
docstr: Function to load pretrained weights including byte embeddings.
docstr_tok: ["Function","to","load","pretrained","weights","including","byte","embeddings","."]
code: def _load_weights(self):
      """
      Function to load pretrained weights including byte embeddings.
      """
      self.npz_weights = np.load(self._weight_file)
      self._load_byte_embedding()
      self._load_cnn_weights()
      self._load_highway()
      self._load_projection()
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from pytorch_translate import common_layers, rnn, utils
  from pytorch_translate.data.dictionary import TAGS
  from torch.nn.utils.rnn import pack_padded_sequence
label_desc: Use image node_id 2 for calling the CharCNNModel obj's underlying member method code with example usage: obj._load_weights() without return types