code: def test_clip(target_precision):
      data = relay.var("data", shape=[1, 10], dtype="float32")
      res = relay.clip(data, a_min=-128000, a_max=128000)
  
      mod = tvm.IRModule.from_expr(res)
  
      mod_params = {
          "data": np.random.uniform(-1, 1, size=[1, 10]).astype(
              "float32"
          ),
      }
      output_mod = verify_mixed_precision_output_close(
          mod,
          mod_params,
          mixed_precision_dtype=target_precision,
          atol=0.01,
          rtol=0.01,
      )
  
      # Create expected module
      if target_precision == "bfloat16":
          data = relay.cast(
              relay.var("data", shape=[1, 10]), target_precision
          )
      res = relay.clip(data, a_min=-128000, a_max=128000)
      expected_mod = tvm.IRModule.from_expr(res)
      expected_mod = InferType()(expected_mod)
      assert tvm.ir.structural_equal(expected_mod, output_mod)
  
imports: from typing import Any, Dict, List
  import numpy
  import pytest
  import tvm
  from tvm import relay
  from tvm.relay.testing import lstm
  from tvm.relay.transform import InferType, ToMixedPrecision, mixed_precision
label_desc: Use image node_id 18 for calling a global function with example usage: test_clip(target_precision) without return types