docstr: Compute the Frechet distance between two sets of statistics.
docstr_tok: ["Compute","the","Frechet","distance","between","two","sets","of","statistics","."]
code: def frechet_distance(self, other, eps=1e-6):
      """
      Compute the Frechet distance between two sets of statistics.
      """
      mu1, sigma1 = self.mu, self.sigma
      mu2, sigma2 = other.mu, other.sigma
  
      mu1 = np.atleast_1d(mu1)
      mu2 = np.atleast_1d(mu2)
  
      sigma1 = np.atleast_2d(sigma1)
      sigma2 = np.atleast_2d(sigma2)
  
      assert (
          mu1.shape == mu2.shape
      ), f"Training and test mean vectors have different lengths: {mu1.shape}, {mu2.shape}"
      assert (
          sigma1.shape == sigma2.shape
      ), f"Training and test covariances have different dimensions: {sigma1.shape}, {sigma2.shape}"
  
      diff = mu1 - mu2
  
      # product might be almost singular
      covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
      if not np.isfinite(covmean).all():
          msg = (
              "fid calculation produces singular product; adding %s to diagonal of cov estimates"
              % eps
          )
          warnings.warn(msg)
          offset = np.eye(sigma1.shape[0]) * eps
          covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
  
      # numerical error might give slight imaginary component
      if np.iscomplexobj(covmean):
          if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
              m = np.max(np.abs(covmean.imag))
              raise ValueError("Imaginary component {}".format(m))
          covmean = covmean.real
  
      tr_covmean = np.trace(covmean)
  
      return (
          diff.dot(diff)
          + np.trace(sigma1)
          + np.trace(sigma2)
          - 2 * tr_covmean
      )
  
imports: from .inception_v3 import InceptionV3
  import blobfile
  import torch
  import torch.distributed
  import torch.nn
  from cm import dist_util
  import numpy
  import warnings
  from scipy import linalg
  from PIL import Image
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the FIDStatistics obj's underlying member method code with example usage: obj.frechet_distance(other, eps) and returns: unknown