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 weights associated with the pretrained projection
  layer. In order to ensure the norm of the weights match up with the
  rest of the model, we need to normalize the pretrained weights.
  Here we divide by a fixed constant.
docstr_tok: ["Function","to","load","the","weights","associated","with","the","pretrained","projection","layer",".","In","order","to","ensure","the","norm","of","the","weights","match","up","with","the","rest","of","the","model",",","we","need","to","normalize","the","pretrained","weights",".","Here","we","divide","by","a","fixed","constant","."]
code: def _load_projection(self):
      """
      Function to load the weights associated with the pretrained projection
      layer. In order to ensure the norm of the weights match up with the
      rest of the model, we need to normalize the pretrained weights.
      Here we divide by a fixed constant.
      """
      input_dim = self.filter_dims
  
      self.projection = nn.Linear(
          input_dim, self.char_cnn_output_dim, bias=True
      )
      weight = self.npz_weights["W_proj"]
      bias = self.npz_weights["b_proj"]
      self.projection.weight.data.copy_(
          torch.div(torch.FloatTensor(np.transpose(weight)), 10.0)
      )
      self.projection.bias.data.copy_(
          torch.div(torch.FloatTensor(np.transpose(bias)), 10.0)
      )
  
      self.projection.weight.requires_grad = (
          self._finetune_pretrained_weights
      )
      self.projection.bias.requires_grad = (
          self._finetune_pretrained_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 6 for calling the CharCNNModel obj's underlying member method code with example usage: obj._load_projection() without return types