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)`
class_docstr_tok: ["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",")","`"]
docstr: Implement for all derived classes 
docstr_tok: ["Implement","for","all","derived","classes"]
code: def fit(self, X, y):
      """Implement for all derived classes"""
  
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 2 for calling the SKLearnLinearSolver obj's underlying member method code with example usage: obj.fit(X, y) without return types