code: def verify_feature_length():
      np.random.seed(123)
      target = "cuda"
      ctx = tvm.device(target)
  
      batch_size = 32
  
      input_shape = (32, 512, 7, 7)
      kernel_shape = (512, 512, 3, 3)
  
      def get_mod():
          x = relay.var("x", relay.TensorType(input_shape, "float32"))
          y = relay.var("y", relay.TensorType(kernel_shape, "float32"))
          f = relay.Function(
              [x, y],
              relay.nn.conv2d(
                  x,
                  y,
                  padding=[1, 1, 1, 1],
                  channels=512,
                  kernel_size=[3, 3],
              ),
          )
          mod = tvm.IRModule()
          mod["main"] = f
          mod = relay.transform.InferType()(mod)
          return mod, {}
  
      mod, params = get_mod()
      layout_config = relay.transform.LayoutConfig()
      desired_layouts = {"nn.conv2d": ["HWNC", "default"]}
      with layout_config:
          seq = tvm.transform.Sequential(
              [relay.transform.ConvertLayout(desired_layouts)]
          )
          with tvm.transform.PassContext(opt_level=3):
              mod = seq(mod)
      mod = relay.transform.recast(mod, "int4", "int32")
  
      tasks = autotvm.task.extract_from_program(
          mod,
          target=target,
          params=params,
          ops=(relay.op.get("nn.conv2d"),),
      )
  
      assert len(tasks) == 1
      task = tasks[0]
  
      space = task.config_space
  
      idx1 = space.get_rand_index()
      idx2 = space.get_rand_index()
  
      cfg = space.get(idx1)
      sch, arg_bufs = task.instantiate(cfg)
      fea1 = autotvm.feature.get_itervar_feature_flatten(
          sch, arg_bufs, take_log=True
      )
  
      cfg = space.get(idx2)
      sch, arg_bufs = task.instantiate(cfg)
      fea2 = autotvm.feature.get_itervar_feature_flatten(
          sch, arg_bufs, take_log=True
      )
  
      assert len(fea1) == len(fea2)
  
file_path: tvm/tests/python/topi/test_topi_conv2d_hwnc_tensorcore.py
imports: import numpy
  import tvm
  import os
  import tvm.testing
  import tvm.topi.testing
  from tvm import te, autotvm, topi, relay
  from tvm.contrib.pickle_memoize import memoize
  from tvm.contrib import nvcc
  from tvm.topi.nn.utils import get_pad_tuple
  from tvm.topi.utils import get_const_tuple
label_desc: Use image node_id 2 for calling a global function with example usage: verify_feature_length() and returns: mod,  dict