code: def test_opencl_args_split(executor_type, shape_type):
      def _get_model():
          if shape_type == "dynamic":
              shape = (tvm.tir.Any(), 1, 1, 3)
          else:
              shape = (1, 1, 1, 3)
          shape_np = (1, 1, 1, 3)
          dtype = "float32"
          axis = 1
          tensors_num = 300
          inputs = []
          inputs_np = {}
          for i in range(tensors_num):
              inputs.append(
                  relay.var("p{}".format(i), shape=shape, dtype=dtype)
              )
              inputs_np[f"p{i}"] = np.random.uniform(
                  size=shape_np
              ).astype(dtype)
  
          inp = relay.Tuple(inputs)
          concat = relay.op.concatenate(inp, axis)
          return inputs_np, relay.Function(inputs, concat)
  
      def ref_impl(inputs):
          axis = 1
          return np.concatenate(tuple(inputs), axis=axis)
  
      if executor_type == "ge" and shape_type == "dynamic":
          pytest.skip()
      _validate_opencl_executors(executor_type, _get_model, ref_impl)
  
file_path: tvm/tests/python/codegen/test_target_codegen_opencl.py
imports: import tvm
  from tvm import te, relay
  import tvm.testing
  import re
  import pytest
  import numpy
label_desc: Use image node_id 9 for calling a global function with example usage: test_opencl_args_split(executor_type, shape_type) and returns: inputs_np,  relay, np