code: def _get_normalized_sim_and_cs(
      preds: Tensor,
      target: Tensor,
      gaussian_kernel: bool = True,
      sigma: Union[float, Sequence[float]] = 1.5,
      kernel_size: Union[int, Sequence[int]] = 11,
      data_range: Optional[Union[float, Tuple[float, float]]] = None,
      k1: float = 0.01,
      k2: float = 0.03,
      normalize: Optional[Literal["relu", "simple"]] = None,
  ) -> Tuple[Tensor, Tensor]:
      sim, contrast_sensitivity = _ssim_update(
          preds,
          target,
          gaussian_kernel,
          sigma,
          kernel_size,
          data_range,
          k1,
          k2,
          return_contrast_sensitivity=True,
      )
      if normalize == "relu":
          sim = torch.relu(sim)
          contrast_sensitivity = torch.relu(contrast_sensitivity)
      return sim, contrast_sensitivity
  
file_path: torchmetrics/src/torchmetrics/functional/image/ssim.py
imports: from typing import List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import functional
  from typing_extensions import Literal
  from torchmetrics.functional.image.helper import _gaussian_kernel_2d, _gaussian_kernel_3d, _reflection_pad_3d
  from torchmetrics.utilities.checks import _check_same_shape
  from torchmetrics.utilities.distributed import reduce
label_desc: Use image node_id 5 for calling a global function with example usage: _get_normalized_sim_and_cs(preds, target, gaussian_kernel, sigma, kernel_size, data_range, k1, k2, normalize) and returns: sim,  contrast_sensitivity