class_docstr: Provide a sklearn-like uniform interface to algorithms that solve problems
  of the form: $y = Ax$ for $x$
  
  Sub-classes of SKLearnLinearSolver should provide a 'fit' method that have
  the following signature: `SKLearnLinearSolver.fit(X, y)`, which would set
  an attribute `SKLearnLinearSolver.coef_`, with the shape (X.shape[1],),
  such that an estimate of y can be calculated as:
  `y_hat = np.dot(X, SKLearnLinearSolver.coef_.T)`
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docstr: Predict using the result of the model
  
  Parameters
  ----------
  X : array-like (n_samples, n_features)
      Samples.
  
  Returns
  -------
  C : array, shape = (n_samples,)
      Predicted values.
docstr_tok: ["Predict","using","the","result","of","the","model","Parameters","--","--","--","--","--","X",":","array-like","(","n_samples",",","n_features",")","Samples",".","Returns","--","--","--","-","C",":","array",",","shape","=","(","n_samples",",",")","Predicted","values","."]
code: def predict(self, X):
      """
      Predict using the result of the model
  
      Parameters
      ----------
      X : array-like (n_samples, n_features)
          Samples.
  
      Returns
      -------
      C : array, shape = (n_samples,)
          Predicted values.
      """
      X = np.asarray(X)
      return np.dot(X, self.coef_.T)
  
imports: import abc
  import warnings
  import numpy
  import scipy.sparse
  import scipy.optimize
  from scipy.optimize import minimize
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 3 for calling the SKLearnLinearSolver obj's underlying member method code with example usage: obj.predict(X) and returns: np