code: def get_model(config):
      import mxnet as mx
      from mxnet import gluon
      from mxnet.gluon import nn
      import mxnet.ndarray as F
  
      class LeNet(gluon.Block):
          def __init__(self, **kwargs):
              super(LeNet, self).__init__(**kwargs)
              with self.name_scope():
                  # layers created in name_scope will inherit name space
                  # from parent layer.
                  self.conv1 = nn.Conv2D(20, kernel_size=(5, 5))
                  self.pool1 = nn.MaxPool2D(
                      pool_size=(2, 2), strides=(2, 2)
                  )
                  self.conv2 = nn.Conv2D(50, kernel_size=(5, 5))
                  self.pool2 = nn.MaxPool2D(
                      pool_size=(2, 2), strides=(2, 2)
                  )
                  self.fc1 = nn.Dense(500)
                  self.fc2 = nn.Dense(10)
  
          def forward(self, x):
              x = self.pool1(F.tanh(self.conv1(x)))
              x = self.pool2(F.tanh(self.conv2(x)))
              # 0 means copy over size from corresponding dimension.
              # -1 means infer size from the rest of dimensions.
              x = x.reshape((0, -1))
              x = F.tanh(self.fc1(x))
              x = F.tanh(self.fc2(x))
              return x
  
      net = LeNet()
      net.initialize(mx.init.Xavier(magnitude=2.24), ctx=[mx.cpu()])
      return net
  
file_path: analytics-zoo/pyzoo/zoo/examples/orca/learn/mxnet/lenet_mnist.py
imports: import argparse
  from zoo.orca import init_orca_context, stop_orca_context
  from zoo.orca.learn.mxnet import Estimator, create_config
label_desc: Use image node_id 3 for calling a global function with example usage: get_model(config) and returns: net, x