class_docstr: Ordinary least squares Linear Regression.
  
  Parameters
  ----------
  fit_intercept : boolean, optional
      whether to calculate the intercept for this model. If set
      to false, no intercept will be used in calculations
      (e.g. data is expected to be already centered).
  
  normalize : boolean, optional, default False
      If True, the regressors X will be normalized before regression.
      This parameter is ignored when `fit_intercept` is set to False.
      When the regressors are normalized, note that this makes the
      hyperparameters learnt more robust and almost independent of the number
      of samples. The same property is not valid for standardized data.
      However, if you wish to standardize, please use
      `preprocessing.StandardScaler` before calling `fit` on an estimator
      with `normalize=False`.
  
  copy_X : boolean, optional, default True
      If True, X will be copied; else, it may be overwritten.
  
  n_jobs : int, optional, default 1
      The number of jobs to use for the computation.
      If -1 all CPUs are used. This will only provide speedup for
      n_targets > 1 and sufficient large problems.
  
  Attributes
  ----------
  coef_ : array, shape (n_features, ) or (n_targets, n_features)
      Estimated coefficients for the linear regression problem.
      If multiple targets are passed during the fit (y 2D), this
      is a 2D array of shape (n_targets, n_features), while if only
      one target is passed, this is a 1D array of length n_features.
  
  residues_ : array, shape (n_targets,) or (1,) or empty
      Sum of residuals. Squared Euclidean 2-norm for each target passed
      during the fit. If the linear regression problem is under-determined
      (the number of linearly independent rows of the training matrix is less
      than its number of linearly independent columns), this is an empty
      array. If the target vector passed during the fit is 1-dimensional,
      this is a (1,) shape array.
  
      .. versionadded:: 0.18
  
  intercept_ : array
      Independent term in the linear model.
  
  Notes
  -----
  From the implementation point of view, this is just plain Ordinary
  Least Squares (scipy.linalg.lstsq) wrapped as a predictor object.
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docstr: Get the residues of the fitted model.
docstr_tok: ["Get","the","residues","of","the","fitted","model","."]
code: def residues_(self):
      """Get the residues of the fitted model."""
      return self._residues
  
imports: from __future__ import division
  from abc import ABCMeta, abstractmethod
  import numbers
  import warnings
  import numpy
  import scipy.sparse
  from scipy import linalg
  from scipy import sparse
  from ..externals import six
  from ..externals.joblib import Parallel, delayed
  from ..base import BaseEstimator, ClassifierMixin, RegressorMixin
  from ..utils import check_array, check_X_y, deprecated, as_float_array
  from ..utils.validation import FLOAT_DTYPES
  from ..utils import check_random_state
  from ..utils.extmath import safe_sparse_dot
  from ..utils.sparsefuncs import mean_variance_axis, inplace_column_scale
  from ..utils.fixes import sparse_lsqr
  from ..utils.seq_dataset import ArrayDataset, CSRDataset
  from ..utils.validation import check_is_fitted
  from ..exceptions import NotFittedError
  from ..preprocessing.data import normalize
label_desc: Use image node_id 2 for calling the LinearRegression obj's underlying member method code with example usage: obj.residues_() and returns: self