code: def get_fid_score(
      y_pred: torch.Tensor, y: torch.Tensor
  ) -> torch.Tensor:
      """Computes the FID score metric on a batch of feature vectors.
  
      Args:
          y_pred: feature vectors extracted from a pretrained network run on generated images.
          y: feature vectors extracted from a pretrained network run on images from the real data distribution.
      """
      y = y.double()
      y_pred = y_pred.double()
  
      if y.ndimension() > 2:
          raise ValueError(
              "Inputs should have (number images, number of features) shape."
          )
  
      mu_y_pred = torch.mean(y_pred, dim=0)
      sigma_y_pred = _cov(y_pred, rowvar=False)
      mu_y = torch.mean(y, dim=0)
      sigma_y = _cov(y, rowvar=False)
  
      return compute_frechet_distance(
          mu_y_pred, sigma_y_pred, mu_y, sigma_y
      )
  
file_path: MONAI/monai/metrics/fid.py
imports: from __future__ import annotations
  import numpy
  import torch
  from monai.metrics.metric import Metric
  from monai.utils import optional_import
label_desc: Use image node_id 1 for calling a global function with example usage: get_fid_score(y_pred, y) and returns: compute_frechet_distance