code: def train_lstm(self, data, verbose=False, **lstm_options):
      x_train, x_test, y_train, y_test = data
      network = algorithms.RMSProp(
          [
              layers.Input(self.n_time_steps),
              layers.Embedding(self.n_categories, 10),
              layers.LSTM(20, **lstm_options),
              layers.Sigmoid(1),
          ],
          step=0.05,
          verbose=verbose,
          batch_size=16,
          loss="binary_crossentropy",
      )
      network.train(x_train, y_train, x_test, y_test, epochs=20)
  
      y_predicted = network.predict(x_test).round()
      accuracy = (y_predicted.T == y_test).mean()
      return accuracy
  
imports: import numpy
  import tensorflow
  from sklearn.model_selection import train_test_split
  from neupy.datasets import reber
  from neupy.utils import asfloat, tensorflow_session
  from neupy.exceptions import LayerConnectionError
  from neupy.layers.recurrent import clip_gradient
  from neupy import layers, algorithms, init
  from base import BaseTestCase
label_desc: Use image node_id 2 for calling the LSTMTestCase obj's underlying member method code with example usage: obj.train_lstm(data, verbose) and returns: accuracy