code: def get_model(inputs_shape):
      ni = Input(inputs_shape)
      nn = Dropout(keep=0.8)(ni)
      nn = Dense(n_units=800, act=tf.nn.relu, in_channels=784)(
          nn
      )  # in_channels is optional in this case as it can be inferred by the previous layer
      nn = Dropout(keep=0.8)(nn)
      nn = Dense(n_units=800, act=tf.nn.relu, in_channels=800)(
          nn
      )  # in_channels is optional in this case as it can be inferred by the previous layer
      nn = Dropout(keep=0.8)(nn)
      nn = Dense(n_units=10, act=tf.nn.relu, in_channels=800)(
          nn
      )  # in_channels is optional in this case as it can be inferred by the previous layer
      M = Model(inputs=ni, outputs=nn)
      return M
  
imports: import os
  import unittest
  import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer.layers import *
  from tensorlayer.models import *
  from tests.utils import CustomTestCase
label_desc: Use image node_id 3 for calling a global function with example usage: get_model(inputs_shape) and returns: M