code: def _cov(X, shrinkage=None):
      """Estimate covariance matrix (using optional shrinkage).
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
          Input data.
  
      shrinkage : string or float, optional
          Shrinkage parameter, possible values:
            - None or 'empirical': no shrinkage (default).
            - 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
            - float between 0 and 1: fixed shrinkage parameter.
  
      Returns
      -------
      s : array, shape (n_features, n_features)
          Estimated covariance matrix.
      """
      shrinkage = "empirical" if shrinkage is None else shrinkage
      if isinstance(shrinkage, string_types):
          if shrinkage == "auto":
              sc = StandardScaler()  # standardize features
              X = sc.fit_transform(X)
              s = ledoit_wolf(X)[0]
              s = (
                  sc.scale_[:, np.newaxis]
                  * s
                  * sc.scale_[np.newaxis, :]
              )  # rescale
          elif shrinkage == "empirical":
              s = empirical_covariance(X)
          else:
              raise ValueError("unknown shrinkage parameter")
      elif isinstance(shrinkage, float) or isinstance(shrinkage, int):
          if shrinkage < 0 or shrinkage > 1:
              raise ValueError(
                  "shrinkage parameter must be between 0 and 1"
              )
          s = shrunk_covariance(empirical_covariance(X), shrinkage)
      else:
          raise TypeError("shrinkage must be of string or int type")
      return s
  
imports: from __future__ import print_function
  import warnings
  import numpy
  from scipy import linalg
  from .externals.six import string_types
  from .externals.six.moves import xrange
  from .base import BaseEstimator, TransformerMixin, ClassifierMixin
  from .linear_model.base import LinearClassifierMixin
  from .covariance import ledoit_wolf, empirical_covariance, shrunk_covariance
  from .utils.multiclass import unique_labels
  from .utils import check_array, check_X_y
  from .utils.validation import check_is_fitted
  from .utils.fixes import bincount
  from .utils.multiclass import check_classification_targets
  from .preprocessing import StandardScaler
label_desc: Use image node_id 1 for calling a global function with example usage: _cov(X, shrinkage) and returns: s