code: def test_convert_follow_node_with_integer_arguments(target_precision):
      """Tests the conversion of a follow op with integer arguments + constant float args.
  
      The follow op should convert the floating point argument into fp16 as constants/vars
      will always be converted if safe to do so.
      """
  
      data = relay.var("data", shape=[1, 10], dtype="float32")
  
      # We use an addition to make sure the input indices are not a var
      # (which are always casted if safe)
      indices = relay.var(
          "indices", shape=[1, 1], dtype="int32"
      ) + relay.const(0, dtype="int32")
      take = relay.take(data, indices, axis=0)
      mod = tvm.IRModule.from_expr(take)
  
      mod_params = {
          "data": np.random.uniform(-1, 1, size=[1, 10]).astype(
              "float32"
          ),
          "indices": np.array([[0]]).astype("int32"),
      }
      output_mod = verify_mixed_precision_output_close(
          mod,
          mod_params,
          mixed_precision_dtype=target_precision,
          atol=0.01,
          rtol=0.01,
      )
  
      # Create expected module
      data = relay.cast(
          relay.var("data", shape=[1, 10]), target_precision
      )
      take = relay.take(data, indices, axis=0)
      expected_mod = tvm.IRModule.from_expr(take)
      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 17 for calling a global function with example usage: test_convert_follow_node_with_integer_arguments(target_precision) without return types