code: def visual_information_fidelity(
      preds: Tensor, target: Tensor, sigma_n_sq: float = 2.0
  ) -> Tensor:
      """Compute Pixel Based Visual Information Fidelity (VIF_).
  
      Args:
          preds: predicted images of shape ``(N,C,H,W)``. ``(H, W)`` has to be at least ``(41, 41)``.
          target: ground truth images of shape ``(N,C,H,W)``. ``(H, W)`` has to be at least ``(41, 41)``
          sigma_n_sq: variance of the visual noise
  
      Return:
          Tensor with vif-p score
  
      Raises:
          ValueError:
              If ``data_range`` is neither a ``tuple`` nor a ``float``
  
      """
      # This code is inspired by
      # https://github.com/photosynthesis-team/piq/blob/01e16b7d8c76bc8765fb6a69560d806148b8046a/piq/vif.py and
      # https://github.com/andrewekhalel/sewar/blob/ac76e7bc75732fde40bb0d3908f4b6863400cc27/sewar/full_ref.py#L357.
  
      if preds.size(-1) < 41 or preds.size(-2) < 41:
          raise ValueError(
              f"Invalid size of preds. Expected at least 41x41, but got {preds.size(-1)}x{preds.size(-2)}!"
          )
  
      if target.size(-1) < 41 or target.size(-2) < 41:
          raise ValueError(
              f"Invalid size of target. Expected at least 41x41, but got {target.size(-1)}x{target.size(-2)}!"
          )
  
      per_channel = [
          _vif_per_channel(
              preds[:, i, :, :], target[:, i, :, :], sigma_n_sq
          )
          for i in range(preds.size(1))
      ]
      return reduce(torch.cat(per_channel), "elementwise_mean")
  
imports: import torch
  from torch import Tensor
  from torch.nn.functional import conv2d
  from torchmetrics.utilities.distributed import reduce
label_desc: Use image node_id 3 for calling a global function with example usage: visual_information_fidelity(preds, target, sigma_n_sq) and returns: reduce