code: def add_context_repr(
      self, ntags, hidden_size=100, height=1, residual=True
  ):
      assert (
          self._word_embeddings_added
          or self._char_cnn_added
          or self._char_bilstm_added,
          "Add word embeddings by method add_word_embeddings "
          + "or add char representation by method add_bilstm_char_repr "
          + "or add_bilstm_char_repr before adding context layer",
      )
  
      self._context_added = True
      self.ntags = ntags
  
      with tf.compat.v1.device("/gpu:{}".format(self.use_gpu_device)):
          context_repr = self._multiply_layer(
              self.word_repr, 2 * hidden_size
          )
          # Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`
          context_repr = tf.nn.dropout(
              x=context_repr, rate=1 - self.dropout
          )
  
          with tf.compat.v1.variable_scope("context_repr"):
              for i in range(height):
                  with tf.compat.v1.variable_scope("lstm-{}".format(i)):
                      new_repr = self._create_lstm_layer(
                          context_repr,
                          hidden_size,
                          lengths=self.sentence_lengths,
                      )
  
                      context_repr = (
                          new_repr + context_repr
                          if residual
                          else new_repr
                      )
  
              context_repr = tf.nn.dropout(
                  x=context_repr, rate=1 - self.dropout
              )
  
              # batch, sentence, ntags
              self.scores = self._multiply_layer(
                  context_repr, ntags, activation=None
              )
  
              tf.identity(self.scores, "scores")
  
              self.predicted_labels = tf.argmax(
                  input=self.scores, axis=-1
              )
              tf.identity(self.predicted_labels, "predicted_labels")
  
imports: import math
  import random
  import sys
  import numpy
  import tensorflow
  from .sentence_grouper import SentenceGrouper
  from ..tf2contrib import *
label_desc: Use image node_id 7 for calling the NerModel obj's underlying member method code with example usage: obj.add_context_repr(ntags, hidden_size, height, residual) without return types