code: def model_keras_dense_and_sparse():
      _NUM_LEARNERS = 3
      inputs = []
      intermediates = []
      for _ in range(_NUM_LEARNERS):
          inp = tf.keras.layers.Input(shape=(1,), dtype=tf.dtypes.int32)
          layer = tf.keras.layers.Embedding(1, 4)(inp)
          layer = tf.keras.layers.Dense(1)(layer)
          inputs.append(inp)
          intermediates.append(layer)
      layer = tf.keras.layers.Concatenate(axis=-1)(intermediates)
      layer = tf.keras.layers.Dense(1)(layer)
      return tf.keras.models.Model(inputs, layer)
  
file_path: autodist/tests/test_graph_item.py
imports: import numpy
  import pytest
  import tensorflow
  from tensorflow.python import ops
  from tensorflow.python.keras.optimizer_v2 import adadelta
  from tensorflow.python.keras.optimizer_v2 import adagrad
  from tensorflow.python.keras.optimizer_v2 import adam
  from tensorflow.python.keras.optimizer_v2 import adamax
  from tensorflow.python.keras.optimizer_v2 import ftrl
  from tensorflow.python.keras.optimizer_v2 import gradient_descent
  from tensorflow.python.keras.optimizer_v2 import nadam
  from tensorflow.python.keras.optimizer_v2 import rmsprop
  from tensorflow.python.training.training_util import get_or_create_global_step
  from autodist import graph_item
  from autodist.kernel.common.op_info import UPDATE_OP_VAR_POS
label_desc: Use image node_id 2 for calling a global function with example usage: model_keras_dense_and_sparse() and returns: tf