class_docstr: Frechet Inception Distance (FID). The FID calculates the distance between two distributions of feature vectors.
  Based on: Heusel M. et al. "Gans trained by a two time-scale update rule converge to a local nash equilibrium."
  https://arxiv.org/abs/1706.08500. The inputs for this metric should be two groups of feature vectors (with format
  (number images, number of features)) extracted from a pretrained network.
  
  Originally, it was proposed to use the activations of the pool_3 layer of an Inception v3 pretrained with Imagenet.
  However, others networks pretrained on medical datasets can be used as well (for example, RadImageNwt for 2D and
  MedicalNet for 3D images). If the chosen model output is not a scalar, a global spatia average pooling should be
  used.
code: def __call__(
      self, y_pred: torch.Tensor, y: torch.Tensor
  ) -> torch.Tensor:
      return get_fid_score(y_pred, 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 the FIDMetric obj's underlying member method code with example usage: obj.__call__(y_pred, y) and returns: get_fid_score