code: def empirical_covariance(X, assume_centered=False):
      """Compute the Maximum likelihood covariance estimator.
  
      Parameters
      ----------
      X : ndarray, shape (n_samples, n_features)
          Data from which to compute the covariance estimate
  
      assume_centered : Boolean
          If True, data are not centered before computation.
          Useful when working with data whose mean is almost, but not exactly
          zero.
          If False, data are centered before computation.
  
      Returns
      -------
      covariance : 2D ndarray, shape (n_features, n_features)
          Empirical covariance (Maximum Likelihood Estimator).
      """
      X = np.asarray(X)
      if X.ndim == 1:
          X = np.reshape(X, (1, -1))
  
      if X.shape[0] == 1:
          warnings.warn(
              "Only one sample available. "
              "You may want to reshape your data array"
          )
  
      if assume_centered:
          covariance = np.dot(X.T, X) / X.shape[0]
      else:
          covariance = np.cov(X.T, bias=1)
  
      if covariance.ndim == 0:
          covariance = np.array([[covariance]])
      return covariance
  
imports: import inspect
  import operator
  import os
  import warnings
  from contextlib import contextmanager
  from io import StringIO
  from math import log
  from pprint import pprint
  import numpy
label_desc: Use image node_id 9 for calling a global function with example usage: empirical_covariance(X, assume_centered) and returns: covariance