old_contents: import numpy as np
  
  
  class BaseEstimator(object):
      X = None
      y = None
      y_required = True
  
      def _setup_input(self, X, y=None):
          """Ensure inputs to an estimator are in the expected format.
  
          Ensures X and y are stored as numpy ndarrays by converting from an
          array-like object if necessary. Enables estimators to define whether
          they require a set of y target values or not with y_required, e.g.
          kmeans clustering requires no target labels and is fit against only X.
  
          Parameters
          ----------
          X : array-like
              Feature dataset.
          y : array-like
              Target values. By default is required, but if y_required = false
              then may be omitted.
          """
          if not isinstance(X, np.ndarray):
              X = np.array(X)
  
          if X.size == 0:
              raise ValueError('Number of features must be > 0')
  
          if X.ndim == 1:
              self.n_samples, self.n_features = 1, X.shape
          else:
              self.n_samples, self.n_features = X.shape[0], np.prod(X.shape[1:])
  
          self.X = X
  
          if self.y_required:
              if y is None:
                  raise ValueError('Missed required argument y')
  
              if not isinstance(y, np.ndarray):
                  y = np.array(y)
  
              if y.size == 0:
                  raise ValueError('Number of targets must be > 0')
  
          self.y = y
  
      def fit(self, X, y=None):
          self._setup_input(X, y)
  
      def predict(self, X=None):
          if self.X is not None:
              return self._predict(X)
          else:
              raise ValueError('You must call `fit` before `predict`')
  
      def _predict(self, X=None):
          raise NotImplementedError()
  
new_contents: import numpy as np
  
  
  class BaseEstimator(object):
      X = None
      y = None
      y_required = True
  
      def _setup_input(self, X, y=None):
          """Ensure inputs to an estimator are in the expected format.
  
          Ensures X and y are stored as numpy ndarrays by converting from an
          array-like object if necessary. Enables estimators to define whether
          they require a set of y target values or not with y_required, e.g.
          kmeans clustering requires no target labels and is fit against only X.
  
          Parameters
          ----------
          X : array-like
              Feature dataset.
          y : array-like
              Target values. By default is required, but if y_required = false
              then may be omitted.
          """
          if not isinstance(X, np.ndarray):
              X = np.array(X)
  
          if X.size == 0:
              raise ValueError('Number of features must be > 0')
  
          if X.ndim == 1:
              self.n_samples, self.n_features = 1, X.shape
          else:
              self.n_samples, self.n_features = X.shape[0], np.prod(X.shape[1:])
  
          self.X = X
  
          if self.y_required:
              if y is None:
                  raise ValueError('Missed required argument y')
  
              if not isinstance(y, np.ndarray):
                  y = np.array(y)
  
              if y.size == 0:
                  raise ValueError('Number of targets must be > 0')
  
          self.y = y
  
      def fit(self, X, y=None):
          self._setup_input(X, y)
  
      def predict(self, X=None):
          if not isinstance(X, np.ndarray):
              X = np.array(X)
  
          if self.X is not None:
              return self._predict(X)
          else:
              raise ValueError('You must call `fit` before `predict`')
  
      def _predict(self, X=None):
          raise NotImplementedError()
  
subject: check if X is numpy array