code: def compute_ssim_and_cs(
      y_pred: torch.Tensor,
      y: torch.Tensor,
      spatial_dims: int,
      kernel_size: Sequence[int],
      kernel_sigma: Sequence[float],
      data_range: float = 1.0,
      kernel_type: KernelType | str = KernelType.GAUSSIAN,
      k1: float = 0.01,
      k2: float = 0.03,
  ) -> tuple[torch.Tensor, torch.Tensor]:
      """
      Function to compute the Structural Similarity Index Measure (SSIM) and Contrast Sensitivity (CS) for a batch
      of images.
  
      Args:
          y_pred: batch of predicted images with shape (batch_size, channels, spatial_dim1, spatial_dim2[, spatial_dim3])
          y: batch of target images with shape (batch_size, channels, spatial_dim1, spatial_dim2[, spatial_dim3])
          kernel_size: the size of the kernel to use for the SSIM computation.
          kernel_sigma: the standard deviation of the kernel to use for the SSIM computation.
          spatial_dims: number of spatial dimensions of the images (2, 3)
          data_range: the data range of the images.
          kernel_type: the type of kernel to use for the SSIM computation. Can be either "gaussian" or "uniform".
          k1: the first stability constant.
          k2: the second stability constant.
  
      Returns:
          ssim: the Structural Similarity Index Measure score for the batch of images.
          cs: the Contrast Sensitivity for the batch of images.
      """
      if y.shape != y_pred.shape:
          raise ValueError(
              f"y_pred and y should have same shapes, got {y_pred.shape} and {y.shape}."
          )
  
      y_pred = convert_data_type(
          y_pred, output_type=torch.Tensor, dtype=torch.float
      )[0]
      y = convert_data_type(
          y, output_type=torch.Tensor, dtype=torch.float
      )[0]
  
      num_channels = y_pred.size(1)
  
      if kernel_type == KernelType.GAUSSIAN:
          kernel = _gaussian_kernel(
              spatial_dims, num_channels, kernel_size, kernel_sigma
          )
      elif kernel_type == KernelType.UNIFORM:
          kernel = torch.ones(
              (num_channels, 1, *kernel_size)
          ) / torch.prod(torch.tensor(kernel_size))
  
      kernel = convert_to_dst_type(src=kernel, dst=y_pred)[0]
  
      c1 = (k1 * data_range) ** 2  # stability constant for luminance
      c2 = (k2 * data_range) ** 2  # stability constant for contrast
  
      conv_fn = getattr(F, f"conv{spatial_dims}d")
      mu_x = conv_fn(y_pred, kernel, groups=num_channels)
      mu_y = conv_fn(y, kernel, groups=num_channels)
      mu_xx = conv_fn(y_pred * y_pred, kernel, groups=num_channels)
      mu_yy = conv_fn(y * y, kernel, groups=num_channels)
      mu_xy = conv_fn(y_pred * y, kernel, groups=num_channels)
  
      sigma_x = mu_xx - mu_x * mu_x
      sigma_y = mu_yy - mu_y * mu_y
      sigma_xy = mu_xy - mu_x * mu_y
  
      contrast_sensitivity = (2 * sigma_xy + c2) / (
          sigma_x + sigma_y + c2
      )
      ssim_value_full_image = (
          (2 * mu_x * mu_y + c1) / (mu_x**2 + mu_y**2 + c1)
      ) * contrast_sensitivity
  
      return ssim_value_full_image, contrast_sensitivity
  
imports: from __future__ import annotations
  import math
  from abc import abstractmethod
  from collections.abc import Callable, Sequence
  from functools import partial
  from typing import Any
  import torch
  import torch.nn.functional
  from monai.metrics.utils import do_metric_reduction
  from monai.utils import MetricReduction, StrEnum, convert_data_type, ensure_tuple_rep
  from monai.utils.type_conversion import convert_to_dst_type
  from .metric import CumulativeIterationMetric
label_desc: Use image node_id 3 for calling a global function with example usage: compute_ssim_and_cs(y_pred, y, spatial_dims, kernel_size, kernel_sigma, data_range, kernel_type, k1, k2) and returns: ssim_value_full_image,  contrast_sensitivity