parameters: (hidden_size)
return_statement: return language_model
docstring: Returns a language model with given hidden size.
docstring_summary: Returns a language model with given hidden size.
docstring_tokens: Returns a language model with given hidden size .
function: def get_language_model(hidden_size):
    """Returns a language model with given hidden size."""
  
    def language_model(X, y):
      inputs = learn.ops.one_hot_matrix(X, 256)
      inputs = tf.unpack(inputs, axis=1)
      target = tf.unpack(y, axis=1)
      encoder_cell = tf.nn.rnn_cell.OutputProjectionWrapper(tf.nn.rnn_cell.GRUCell(hidden_size),256)
      output, _ = tf.nn.rnn(encoder_cell, inputs, dtype=tf.float32)
      return learn.ops.sequence_classifier(output, target)
  
    return language_model
function_tokens: def get_language_model ( hidden_size ) : def language_model ( X , y ) : inputs = learn . ops . one_hot_matrix ( X , 256 ) inputs = tf . unpack ( inputs , axis = 1 ) target = tf . unpack ( y , axis = 1 ) encoder_cell = tf . nn . rnn_cell . OutputProjectionWrapper ( tf . nn . rnn_cell . GRUCell ( hidden_size ) , 256 ) output , _ = tf . nn . rnn ( encoder_cell , inputs , dtype = tf . float32 ) return learn . ops . sequence_classifier ( output , target ) return language_model