class_docstr: Maximum likelihood covariance estimator.
  
  Read more in the :ref:`User Guide <covariance>`.
  
  Parameters
  ----------
  store_precision : bool
      Specifies if the estimated precision is stored.
  
  assume_centered : bool
      If True, data are not centered before computation.
      Useful when working with data whose mean is almost, but not exactly
      zero.
      If False (default), data are centered before computation.
  
  Attributes
  ----------
  covariance_ : 2D ndarray, shape (n_features, n_features)
      Estimated covariance matrix
  
  precision_ : 2D ndarray, shape (n_features, n_features)
      Estimated pseudo-inverse matrix.
      (stored only if store_precision is True)
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docstr: Fit the Maximum Likelihood Estimator covariance model.
  
  Parameters
  ----------
  X : array-like, shape = [n_samples, n_features]
    Training data, where n_samples is the number of samples and
    n_features is the number of features.
  y : ndarray | None
      Not used, present for API consistency.
  
  Returns
  -------
  self : object
      Returns self.
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code: def fit(self, X, y=None):
      """Fit the Maximum Likelihood Estimator covariance model.
  
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
        Training data, where n_samples is the number of samples and
        n_features is the number of features.
      y : ndarray | None
          Not used, present for API consistency.
  
      Returns
      -------
      self : object
          Returns self.
      """  # noqa: E501
      # X = check_array(X)
      if self.assume_centered:
          self.location_ = np.zeros(X.shape[1])
      else:
          self.location_ = X.mean(0)
      covariance = empirical_covariance(
          X, assume_centered=self.assume_centered
      )
      self._set_covariance(covariance)
  
      return self
  
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 4 for calling the EmpiricalCovariance obj's underlying member method code with example usage: obj.fit(X, y) and returns: self