code: def test_argparse_normalize(self) -> None:
      input_transform_kwargs = input_transform_argparse(
          Normalize,
          dataset=self.dataset,
          search_space_digest=self.search_space_digest,
      )
  
      self.assertTrue(
          torch.all(
              torch.isclose(
                  input_transform_kwargs["bounds"],
                  torch.tensor(
                      [[0.0, 0.0, 0.0], [1.0, 2.0, 4.0]],
                      dtype=input_transform_kwargs["bounds"].dtype,
                  ),
              )
          )
      )
      self.assertEqual(input_transform_kwargs["d"], 4)
  
      input_transform_kwargs = input_transform_argparse(
          Normalize,
          dataset=self.dataset,
          search_space_digest=self.search_space_digest,
          input_transform_options={
              "d": 4,
              "bounds": torch.tensor(
                  [[0.0, 0.0, 0.0], [1.0, 2.0, 4.0]],
                  dtype=torch.float64,
              ),
          },
      )
  
      self.assertEqual(input_transform_kwargs["d"], 4)
  
      self.assertTrue(
          torch.all(
              torch.isclose(
                  input_transform_kwargs["bounds"],
                  torch.tensor(
                      [[0.0, 0.0, 0.0], [1.0, 2.0, 4.0]],
                      dtype=torch.float64,
                  ),
              )
          )
      )
  
      # Test with MultiTaskDataset.
      dataset1 = SupervisedDataset(
          X=torch.rand(5, 4),
          Y=torch.randn(5, 1),
          feature_names=[f"x{i}" for i in range(4)],
          outcome_names=["y0"],
      )
  
      dataset2 = SupervisedDataset(
          X=torch.rand(5, 2),
          Y=torch.randn(5, 1),
          feature_names=[f"x{i}" for i in range(2)],
          outcome_names=["y1"],
      )
      mtds = MultiTaskDataset(
          datasets=[dataset1, dataset2], target_outcome_name="y0"
      )
      input_transform_kwargs = input_transform_argparse(
          Normalize,
          dataset=mtds,
          search_space_digest=self.search_space_digest,
      )
      self.assertEqual(input_transform_kwargs["d"], 4)
      self.assertEqual(input_transform_kwargs["indices"], [0, 1, 2])
  
imports: from __future__ import annotations
  from unittest.mock import patch
  import numpy
  import torch
  from ax.core.search_space import RobustSearchSpaceDigest, SearchSpaceDigest
  from ax.models.torch.botorch_modular.input_constructors.input_transforms import input_transform_argparse
  from ax.utils.common.testutils import TestCase
  from botorch.models.transforms.input import InputPerturbation, InputStandardize, InputTransform, Normalize, Warp
  from botorch.utils.datasets import MultiTaskDataset, SupervisedDataset
label_desc: Use image node_id 6 for calling the InputTransformArgparseTest obj's underlying member method code with example usage: obj.test_argparse_normalize() without return types