code: def create_model(self, frame_mode=False):
      if frame_mode:
          self.feat = cntk.input_variable(shape=(feat_dim,))
          self.label = cntk.input_variable((label_dim,))
  
          net = cntk.layers.Sequential(
              [
                  cntk.layers.Dense(cell_dim),
                  cntk.layers.Dense(label_dim),
              ]
          )
          self.output = net(self.feat)
      else:
          # sequence mode
          self.feat = cntk.sequence.input_variable(shape=(feat_dim,))
          self.label = cntk.sequence.input_variable((label_dim,))
  
          net = cntk.layers.Sequential(
              [
                  cntk.layers.Recurrence(
                      cntk.layers.LSTM(
                          shape=label_dim, cell_shape=(cell_dim,)
                      )
                  )
              ]
          )
          self.output = net(self.feat)
  
      self.ce = cntk.cross_entropy_with_softmax(self.output, self.label)
      self.err = cntk.classification_error(self.output, self.label)
  
imports: import pytest
  import cntk
  import numpy
  import sys, os
  import argparse
  import re
  import platform
  from distributed_learner_test import mpiexec_execute
  from bmuf_metrics_aggregation_test import get_minibatch
label_desc: Use image node_id 2 for calling the SingleDataParallelTrainer obj's underlying member method code with example usage: obj.create_model(frame_mode) without return types