import tensorflow as tf

      outputs = tf.unstack(outputs, axis=0)
    else:
      lstm_input = tf.unstack(x, self.time_steps, 1)
      outputs, _ = tf.nn.static_rnn(lstm_layer, lstm_input, dtype="float32")

    # Compute logits by multiplying outputs[-1] of shape [batch_size,num_units]
    # by the softmax layer's out_weight of shape [num_units,n_classes]
    # plus out_bias
    prediction = tf.matmul(outputs[-1], out_weights) + out_bias
    output_class = tf.nn.softmax(prediction, name="OUTPUT_CLASS")

    return x, prediction, output_class

  def trainModel(self, x, prediction, output_class, sess):
    """Train the model.

    Args:
      x: The input tensor.
