code: def empirical_covariances(
      subjects, assume_centered=False, standardize=False
  ):
      """Compute empirical covariances for several signals.
  
      Parameters
      ----------
      subjects : list of numpy.ndarray, shape for each (n_samples, n_features)
          input subjects. Each subject is a 2D array, whose columns contain
          signals. Sample number can vary from subject to subject, but all
          subjects must have the same number of features (i.e. of columns).
  
      assume_centered : bool, default=False
          if True, assume that all input signals are centered. This slightly
          decreases computation time by avoiding useless computation.
  
      standardize : bool, default=False
          if True, set every signal variance to one before computing their
          covariance matrix (i.e. compute a correlation matrix).
  
      Returns
      -------
      emp_covs : numpy.ndarray, \
          shape : (feature number, feature number, subject number)
          empirical covariances.
  
      n_samples : numpy.ndarray, shape: (subject number,)
          number of samples for each subject. dtype is np.float64.
  
      """
      if not hasattr(subjects, "__iter__"):
          raise ValueError(
              "'subjects' input argument must be an iterable. "
              f"You provided {subjects.__class__}"
          )
  
      n_subjects = [s.shape[1] for s in subjects]
      if len(set(n_subjects)) > 1:
          raise ValueError(
              "All subjects must have the same number of "
              f"features.\nYou provided: {n_subjects}"
          )
      n_subjects = len(subjects)
      n_features = subjects[0].shape[1]
  
      # Enable to change dtype here because depending on user, conversion from
      # single precision to double will be required or not.
      emp_covs = np.empty(
          (n_features, n_features, n_subjects), order="F"
      )
      for k, s in enumerate(subjects):
          if standardize:
              s = s / s.std(axis=0)  # copy on purpose
          M = empirical_covariance(s, assume_centered=assume_centered)
  
          # Force matrix symmetry, for numerical stability
          # of _group_sparse_covariance
          emp_covs[..., k] = M + M.T
      emp_covs /= 2
  
      n_samples = np.asarray(
          [s.shape[0] for s in subjects], dtype=np.float64
      )
  
      return emp_covs, n_samples
  
imports: import collections.abc
  import itertools
  import operator
  import warnings
  import numpy
  import scipy.linalg
  from joblib import Memory, Parallel, delayed
  from sklearn.base import BaseEstimator
  from sklearn.covariance import empirical_covariance
  from sklearn.model_selection import check_cv
  from sklearn.utils.extmath import fast_logdet
  from .._utils import CacheMixin, logger
  from .._utils.extmath import is_spd
label_desc: Use image node_id 6 for calling a global function with example usage: empirical_covariances(subjects, assume_centered, standardize) and returns: emp_covs,  n_samples