code: def get_network():
      # Get a list of modules representing subgraphs.
      mods = []
      dshape = (3, 3)
      data = relay.var("data_0", relay.TensorType(dshape, "float32"))
      data21 = relay.var("data_1", relay.TensorType(dshape, "float32"))
      data_net1_output_1 = relay.var(
          "data_0", relay.TensorType(dshape, "float32")
      )
      data_net1_output_2 = relay.var(
          "data_1", relay.TensorType(dshape, "float32")
      )
      data_net2_output_1 = relay.var(
          "data_0", relay.TensorType(dshape, "float32")
      )
      mvalue1 = np.full((1), 1).astype("float32")
      mvalue2 = np.full((1), 2).astype("float32")
      mvalue3 = np.full((1), 3).astype("float32")
      mv1 = relay.Constant(tvm.nd.array(mvalue1))
      mv2 = relay.Constant(tvm.nd.array(mvalue2))
      mv3 = relay.Constant(tvm.nd.array(mvalue3))
      # There are three outputs in the first model.
      net1_output1 = relay.add(data, mv1)
      net1_output2 = relay.subtract(data, mv2)
      net1_output3 = relay.concatenate(
          (net1_output1, net1_output2), axis=0
      )
      (net1_output3, _) = relay.split(
          net1_output3, indices_or_sections=2, axis=0
      )
      net1_output3 = relay.add(net1_output3, mv2)
      # The second model uses the output named net1_output3 of the first model as the first input,
      # the second input of the second model is data21.
      net2 = relay.add(net1_output3, mv2)
      net2 = relay.add(net2, data21)
      net2_output = relay.add(net2, mv3)
      # The third model uses the output named net2_output of the second model as the first input
      # and uses the output named net1_output2 of the first model as the second input.
      net3 = relay.multiply(net2_output, mv3)
      net3 = relay.add(net3, net1_output2)
      return (
          tvm.IRModule.from_expr(
              relay.Function([data, data21], relay.Tuple([net3]))
          ),
          dshape,
      )
  
imports: import pytest
  import os
  import time
  import numpy
  import tvm
  import tvm.testing
  from tvm import relay
  from tvm.relay import transform, build_module
  from tvm.relay.testing import run_opt_pass
  from tvm.contrib import graph_executor, pipeline_executor, pipeline_executor_build
  from tvm._ffi import get_global_func
  from tvm.contrib import cc
label_desc: Use image node_id 2 for calling a global function with example usage: get_network() and returns: tvm,  dshape