code: def test_opencl_fuse_max_args(executor_type, shape_type):
      if shape_type == "dynamic":
          shape = (tvm.tir.Any(), 20)
          ops_num = 80
      else:
          shape = (1, 20)
          ops_num = 300
      shape_np = (1, 20)
      dtype = "float32"
  
      def _base_func(name):
          x = relay.var(name, shape=shape)
          y = relay.add(x, relay.const(1, "float32"))
          w = relay.exp(y)
          return x, w
  
      def _get_model():
          inp = []
          inputs_np = {}
          out = []
          for i in range(ops_num):
              x, w = _base_func(f"x{i}")
              inputs_np[f"x{i}"] = np.random.uniform(
                  size=shape_np
              ).astype(dtype)
              inp.append(x)
              out.append(w)
          w = out[0]
          for i in range(len(out) - 1):
              w = relay.add(w, out[i + 1])
          return inputs_np, relay.Function(inp, w)
  
      def ref_impl(inputs):
          w = np.exp(inputs[0] + 1)
          for i in range(len(inputs) - 1):
              w = w + np.exp(inputs[i + 1] + 1)
          return w
  
      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 10 for calling a global function with example usage: test_opencl_fuse_max_args(executor_type, shape_type) and returns: x,  w, inputs_np,  relay, w