code: def test_tflite_output_multiplier_greater_than_one():
      with TempOpAttr(
          "qnn.conv2d", "FTVMQnnLegalize", legalize_qnn_conv2d
      ):
          # uint8 input
          data_shape = (2, 1, 2, 4)
          data_dtype = "uint8"
          kernel_shape = (3, 1, 2, 2)
          kernel_dtype = "uint8"
          ref_func, qnn_func = get_funcs(
              data_shape=data_shape,
              data_dtype=data_dtype,
              kernel_shape=kernel_shape,
              kernel_dtype=kernel_dtype,
              input_scale=1.0,
              kernel_scale=1.0,
              input_zero_point=128,
              kernel_zero_point=128,
              kernel_size=(2, 2),
              padding=(0, 0),
              strides=(2, 2),
              dilation=(1, 1),
              data_layout="NCHW",
              kernel_layout="OIHW",
              out_dtype="int32",
          )
          golden_data = 128 + np.array(
              (1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 3, 4, 1, 2, 3, 4)
          ).reshape(data_shape).astype("uint8")
          golden_weight = 128 + np.array(
              (1, 2, 3, 4, -1, 1, -1, 1, -1, -1, 1, 1)
          ).reshape(kernel_shape)
          golden_weight = golden_weight.astype("uint8")
  
          with tvm.transform.PassContext(opt_level=2):
              params = {"kernel": golden_weight}
              graph, lib, params = relay.build(
                  qnn_func, "llvm", params=params
              )
              mod = graph_executor.create(graph, lib, device=tvm.cpu(0))
              mod.set_input("data", golden_data)
              mod.set_input(**params)
              mod.run()
              qnn_output = mod.get_output(0).numpy()
          golden_output = np.array(
              (17, 17, 0, 0, 2, 2, 16, 36, 2, 2, 0, 0)
          ).reshape(2, 3, 1, 2)
          np.testing.assert_equal(qnn_output, golden_output)
  
imports: import tvm
  from tvm import te
  import numpy
  from tvm import relay
  from tvm.relay import transform
  from tvm.relay.testing import run_infer_type
  from tvm.contrib import graph_executor
  from tvm.relay.testing.temp_op_attr import TempOpAttr
label_desc: Use image node_id 18 for calling a global function with example usage: test_tflite_output_multiplier_greater_than_one() without return types