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 the pre-trained byte embeddings. We need to ensure that
  the embeddings account for special yoda tags as well.
docstr_tok: ["Function","to","load","the","pre-trained","byte","embeddings",".","We","need","to","ensure","that","the","embeddings","account","for","special","yoda","tags","as","well","."]
code: def _load_byte_embedding(self):
      """
      Function to load the pre-trained byte embeddings. We need to ensure that
      the embeddings account for special yoda tags as well.
      """
      char_embed_weights = self.npz_weights["char_embed"]
  
      num_tags = TAGS.__len__()
      weights = np.zeros(
          (
              char_embed_weights.shape[0] + num_tags + 1,
              char_embed_weights.shape[1],
          ),
          dtype="float32",
      )
      weights[1:-num_tags, :] = char_embed_weights
  
      self.embed_chars = rnn.Embedding(
          num_embeddings=self.num_embeddings,
          embedding_dim=self.char_embed_dim,
          padding_idx=self.padding_idx,
          freeze_embed=self._finetune_pretrained_weights,
      )
      self.embed_chars.weight.data.copy_(torch.FloatTensor(weights))
  
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 3 for calling the CharCNNModel obj's underlying member method code with example usage: obj._load_byte_embedding() without return types