code: def get_expected_output_module():
      """Returns manually created expected output module."""
      out_q1 = relay.var("q1", shape=(1, 64, 35, 35), dtype="uint8")
      out_q2 = relay.var("q2", shape=(1, 64, 35, 35), dtype="uint8")
      out_q3 = relay.var("q3", shape=(1, 32, 35, 35), dtype="uint8")
      out_s2 = relay.const(0.000109401, dtype="float32")
      out_s3 = relay.const(0.0486874, dtype="float32")
      out_s4 = relay.const(0.0425042, dtype="float32")
      out_z1 = relay.const(0, dtype="int32")
      nn_max_pool = relay.op.nn.max_pool2d(
          out_q1,
          pool_size=[3, 3],
          strides=[1, 1],
          padding=[1, 1],
          dilation=[1, 1],
          ceil_mode=False,
          layout="NHWC",
      )
      out_r1 = relay.qnn.requantize(
          nn_max_pool,
          out_s4,
          out_z1,
          out_s3,
          out_z1,
          axis=1,
          out_dtype="uint8",
      )
      out_r2 = relay.qnn.requantize(
          out_q2,
          out_s2,
          out_z1,
          out_s3,
          out_z1,
          axis=1,
          out_dtype="uint8",
      )
      out_q_tuple = relay.expr.Tuple([out_r1, out_r2, out_q3])
      out_graph = relay.op.concatenate(out_q_tuple, axis=1)
  
      out_func = relay.Function(
          relay.analysis.free_vars(out_graph), out_graph
      )
      out_mod = tvm.IRModule.from_expr(out_func)
      return out_mod
  
imports: import tvm
  from tvm import relay, testing
  from tvm.contrib.hexagon.transform import simplify_qnn_concat
label_desc: Use image node_id 2 for calling a global function with example usage: get_expected_output_module() and returns: out_mod