docstr: The mean accuracy on the given data and labels
  
  Parameters
  ----------
  X : array-like, shape = [n_samples, n_features]
      Test samples.
  y : array-like, shape = [n_samples,]
      Test labels.
  
  Returns
  -------
  score : float
      Mean accuracy score
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code: def score(self, X, y):
      """The mean accuracy on the given data and labels
  
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
          Test samples.
      y : array-like, shape = [n_samples,]
          Test labels.
  
      Returns
      -------
      score : float
          Mean accuracy score
      """
      return accuracy_score(y, self.predict(X))
  
imports: import textwrap
  from dask_glm import algorithms, families
  from dask_glm.utils import accuracy_score, add_intercept, dot, exp, poisson_deviance, sigmoid
  from sklearn.base import BaseEstimator
  from ..metrics import r2_score
  from ..utils import check_array
  from .utils import lr_prob_stack
label_desc: Use image node_id 5 for calling the LogisticRegression obj's underlying member method code with example usage: obj.score(X, y) and returns: accuracy_score