class_docstr: The Passthrough estimator allows users to specify pre-predicted results to
  Yellowbrick without the need to input the original estimator. Note that Yellowbrick
  often uses the learned attributes of the estimator to produce rich visual
  diagnostics, so this estimator may not work for all Yellowbrick visualizers.
  
  The passthrough estimator can accept data either in memory as a numpy array or it
  can accept a string, which it interprets as a path on disk to load the data from.
  
  Currently passthrough does not support predict_proba or decision_function methods,
  which it could if it was passed predicted data as 2D array instead of a 1D array.
  
  Parameters
  ----------
  data : array-like, func, or file-like object, string, or pathlib.Path
      The predicted values wrapped by the estimator and returned on predict() and
      used by the score function. The default expectation is that data is a 1D numpy
      array of y_hat or y_pred values produced by some other estimator. Data can also
      be a func, which is called and returned, or a file-like object, string, or
      pathlib.Path at which point the data is loaded from disk using ``np.load``.
  
  estimator_type : str, optional
      One of "classifier", "regressor", "clusterer", "DensityEstimator", or
      "outlier_detector" that allows the contrib estimator to pass the scikit-learn
      ``is_classifier``, etc. functions. If not specified, the Yellowbrick visualizer
      you're trying to use may error.
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docstr: Fit is a no-op, simply returning self per the scikit-learn API.
docstr_tok: ["Fit","is","a","no-op",",","simply","returning","self","per","the","scikit-learn","API","."]
code: def fit(self, X, y=None):
      """
      Fit is a no-op, simply returning self per the scikit-learn API.
      """
      return self
  
file_path: yellowbrick/yellowbrick/contrib/prepredict.py
imports: import pathlib
  import numpy
  from sklearn.base import BaseEstimator
  from sklearn.metrics import accuracy_score, r2_score, silhouette_score
  from yellowbrick.contrib.wrapper import CLASSIFIER, CLUSTERER, REGRESSOR
label_desc: Use image node_id 2 for calling the PrePredict obj's underlying member method code with example usage: obj.fit(X, y) and returns: self