code: def get_net(
      batch_size,
      num_classes=10,
      image_shape=(1, 28, 28),
      dtype="float32",
  ):
      """Get network a simple multilayer perceptron.
  
      batch_size : int
          The batch size used in the model
  
      num_classes : int, optional
          Number of claseses
  
      image_shape : tuple, optional
          The input image shape
  
      dtype : str, optional
          The data type
  
      Returns
      -------
      net : relay.Function
          The dataflow.
      """
      data_shape = (batch_size,) + image_shape
      data = relay.var("data", shape=data_shape, dtype=dtype)
      data = relay.nn.batch_flatten(data)
      fc1 = relay.nn.dense(data, relay.var("fc1_weight"), units=128)
      fc1 = relay.nn.bias_add(fc1, relay.var("fc1_bias"), axis=-1)
      act1 = relay.nn.relu(fc1)
      fc2 = relay.nn.dense(act1, relay.var("fc2_weight"), units=64)
      fc2 = relay.nn.bias_add(fc2, relay.var("fc2_bias"), axis=-1)
      act2 = relay.nn.relu(fc2)
      fc3 = relay.nn.dense(
          act2, relay.var("fc3_weight"), units=num_classes
      )
      fc3 = relay.nn.bias_add(fc3, relay.var("fc3_bias"), axis=-1)
      mlp = relay.nn.softmax(data=fc3)
      args = relay.analysis.free_vars(mlp)
      return relay.Function(args, mlp)
  
imports: from __future__ import absolute_import
  from tvm import relay
  from .init import create_workload
label_desc: Use image node_id 1 for calling a global function with example usage: get_net(batch_size, num_classes, image_shape, dtype) and returns: relay