class_docstr: The character CNN + highway encoder from
  [Kim et al "Character aware neural language models"](https://arxiv.org/abs/1508.06615)
  with an optional projection.
  
  Registered as a `Seq2VecEncoder` with name "cnn-highway".
  
  # Parameters
  
  embedding_dim : `int`, required
      The dimension of the initial character embedding.
  filters : `Sequence[Sequence[int]]`, required
      A sequence of pairs (filter_width, num_filters).
  num_highway : `int`, required
      The number of highway layers.
  projection_dim : `int`, required
      The output dimension of the projection layer.
  activation : `str`, optional (default = `'relu'`)
      The activation function for the convolutional layers.
  projection_location : `str`, optional (default = `'after_highway'`)
      Where to apply the projection layer. Valid values are
      'after_highway', 'after_cnn', and None.
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code: def get_input_dim(self) -> int:
      return self.input_dim
  
file_path: allennlp/allennlp/modules/seq2vec_encoders/cnn_highway_encoder.py
imports: from typing import Sequence, Dict, List, Callable
  import torch
  import numpy
  from allennlp.common.checks import ConfigurationError
  from allennlp.modules.layer_norm import LayerNorm
  from allennlp.modules.highway import Highway
  from allennlp.modules.seq2vec_encoders.seq2vec_encoder import Seq2VecEncoder
label_desc: Use image node_id 3 for calling the CnnHighwayEncoder obj's underlying member method code with example usage: obj.get_input_dim() and returns: self