class_docstr: Denormalize Torch Tensor into np image format.
docstr: Denormalize Torch Tensor into np image format.
  
  Args:
      mean: Mean
      std: Standard deviation.
code: def __init__(
      self,
      mean: list[float] | None = None,
      std: list[float] | None = None,
  ) -> None:
      """Denormalize Torch Tensor into np image format.
  
      Args:
          mean: Mean
          std: Standard deviation.
      """
      warnings.warn(
          "Denormalize is no longer used and will be deprecated in v0.4.0"
      )
      # If no mean and std provided, assign ImageNet values.
      if mean is None:
          mean = [0.485, 0.456, 0.406]
  
      if std is None:
          std = [0.229, 0.224, 0.225]
  
      self.mean = Tensor(mean)
      self.std = Tensor(std)
  
file_path: anomalib/src/anomalib/pre_processing/transforms/custom.py
imports: from __future__ import annotations
  import warnings
  import numpy
  import torch
  from torch import Tensor
label_desc: Use image node_id 1 to create a new Denormalize object  with example: obj = Denormalize(mean, std)