code: def test_outlier_replacer():
      replacer = _OutlierReplacer(
          {
              "mean": 50,
              "std": 30,
              "preprocessing": {
                  "outlier_threshold": 2.0,
                  "computed_outlier_fill_value": 42,
              },
          }
      )
  
      t = torch.from_numpy(
          np.array([10, 20, 1000, -500, 80], dtype=np.float32)
      )
      t_out_expected = torch.from_numpy(
          np.array([10, 20, 42, 42, 80], dtype=np.float32)
      )
  
      t_out = replacer(t)
      assert torch.equal(t_out, t_out_expected)
  
file_path: ludwig/tests/ludwig/features/test_number_feature.py
imports: from copy import deepcopy
  from typing import Dict
  import numpy
  import pytest
  import torch
  from ludwig.constants import ENCODER_OUTPUT
  from ludwig.features.number_feature import _OutlierReplacer, NumberInputFeature
  from ludwig.schema.features.number_feature import ECDNumberInputFeatureConfig
  from ludwig.schema.utils import load_config_with_kwargs
  from ludwig.utils.misc_utils import merge_dict
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 3 for calling a global function with example usage: test_outlier_replacer() without return types