code: def denormalize(tensor):
      """Reverses the normalisation on a tensor.
  
      Performs a reverse operation on a tensor, so the pixel value range is
      between 0 and 1. Useful for when plotting a tensor into an image.
  
      Normalisation: (image - mean) / std
      Denormalisation: image * std + mean
  
      Args:
          tensor (torch.Tensor, dtype=torch.float32): Normalized image tensor
  
      Shape:
          Input: :math:`(N, C, H, W)`
          Output: :math:`(N, C, H, W)` (same shape as input)
  
      Return:
          torch.Tensor (torch.float32): Demornalised image tensor with pixel
              values between [0, 1]
  
      Note:
          Symbols used to describe dimensions:
              - N: number of images in a batch
              - C: number of channels
              - H: height of the image
              - W: width of the image
  
      """
  
      means = [0.485, 0.456, 0.406]
      stds = [0.229, 0.224, 0.225]
  
      denormalized = tensor.clone()
  
      for channel, mean, std in zip(denormalized[0], means, stds):
          channel.mul_(std).add_(mean)
  
      return denormalized
  
file_path: flashtorch/flashtorch/utils/__init__.py
imports: from PIL import Image
  import torchvision.transforms
  import torchvision.transforms.functional
  from .imagenet import *
label_desc: Use image node_id 3 for calling a global function with example usage: denormalize(tensor) and returns: denormalized