code: def test_TorchNormalizer_dtype_consistency():
      """
      - Ensures that even for float64 `target_scale`, the transformation will not change the prediction dtype.
      - Ensure that target_scale will be of type float32 if method is 'identity'
      """
      parameters = torch.tensor([[[366.4587]]])
      target_scale = torch.tensor(
          [[427875.7500, 80367.4766]], dtype=torch.float64
      )
      assert (
          TorchNormalizer()(
              dict(prediction=parameters, target_scale=target_scale)
          ).dtype
          == torch.float32
      )
      assert (
          TorchNormalizer()
          .transform(parameters, target_scale=target_scale)
          .dtype
          == torch.float32
      )
  
      y = np.array([1, 2, 3], dtype=np.float32)
      assert (
          TorchNormalizer(method="identity")
          .fit(y)
          .get_parameters()
          .dtype
          == torch.float32
      )
  
file_path: pytorch-forecasting/tests/test_data/test_encoders.py
imports: from copy import deepcopy
  import itertools
  import numpy
  import pandas
  import pytest
  from sklearn.utils.validation import NotFittedError, check_is_fitted
  import torch
  from pytorch_forecasting.data import EncoderNormalizer, GroupNormalizer, MultiNormalizer, NaNLabelEncoder, TorchNormalizer
label_desc: Use image node_id 7 for calling a global function with example usage: test_TorchNormalizer_dtype_consistency() without return types