code: def train_sequence_classifier():
      input_dim = 2000
      cell_dim = 25
      hidden_dim = 25
      embedding_dim = 50
      num_output_classes = 5
  
      # Input variables denoting the features and label data
      features = sequence.input_variable(
          shape=input_dim, is_sparse=True
      )
      label = input_variable(num_output_classes)
  
      # Instantiate the sequence classification model
      classifier_output = LSTM_sequence_classifier_net(
          features,
          num_output_classes,
          embedding_dim,
          hidden_dim,
          cell_dim,
      )
  
      ce = cross_entropy_with_softmax(classifier_output, label)
      pe = classification_error(classifier_output, label)
  
      rel_path = (
          "../../../Tests/EndToEndTests/Text/"
          + "SequenceClassification/Data/Train.ctf"
      )
      path = os.path.join(
          os.path.dirname(os.path.abspath(__file__)), rel_path
      )
  
      reader = create_reader(path, True, input_dim, num_output_classes)
  
      input_map = {
          features: reader.streams.features,
          label: reader.streams.labels,
      }
  
      lr_per_sample = learning_parameter_schedule_per_sample(0.0005)
      # Instantiate the trainer object to drive the model training
      progress_printer = ProgressPrinter(0)
      trainer = Trainer(
          classifier_output,
          (ce, pe),
          sgd(classifier_output.parameters, lr=lr_per_sample),
          progress_printer,
      )
  
      # Get minibatches of sequences to train with and perform model training
      minibatch_size = 200
  
      for i in range(255):
          mb = reader.next_minibatch(
              minibatch_size, input_map=input_map
          )
          trainer.train_minibatch(mb)
  
      evaluation_average = float(
          trainer.previous_minibatch_evaluation_average
      )
      loss_average = float(trainer.previous_minibatch_loss_average)
      return evaluation_average, loss_average
  
file_path: CNTK/bindings/python/doc/simplernn.py
imports: import sys
  import os
  from cntk import Trainer, Axis
  from cntk.io import MinibatchSource, CTFDeserializer, StreamDef, StreamDefs, INFINITELY_REPEAT
  from cntk.learners import sgd, learning_parameter_schedule_per_sample
  from cntk import input_variable, cross_entropy_with_softmax, classification_error, sequence
  from cntk.logging import ProgressPrinter
  from cntk.layers import Sequential, Embedding, Recurrence, LSTM, Dense
label_desc: Use image node_id 3 for calling a global function with example usage: train_sequence_classifier() and returns: evaluation_average,  loss_average