class_docstr: Base class for Linear Models
class_docstr_tok: ["Base","class","for","Linear","Models"]
docstr: Set the intercept_
docstr_tok: ["Set","the","intercept_"]
code: def _set_intercept(self, X_offset, y_offset, X_scale):
      """Set the intercept_"""
      if self.fit_intercept:
          self.coef_ = self.coef_ / X_scale
          self.intercept_ = y_offset - mt.dot(X_offset, self.coef_.T)
          execute(self.coef_, self.intercept_)
      else:
          self.intercept_ = mt.tensor(0.0)
          self.intercept_.execute()
  
imports: import numbers
  from abc import ABCMeta, abstractmethod
  from numpy.linalg import LinAlgError
  from sklearn.utils.validation import check_is_fitted, _deprecate_positional_args
  from sklearn.base import MultiOutputMixin
  from ...None import execute
  from ...None import tensor
  from ...tensor.datasource import tensor
  from ..base import BaseEstimator, RegressorMixin, ClassifierMixin
  from ..preprocessing import normalize
  from ..utils.validation import _check_sample_weight, check_array, FLOAT_DTYPES
label_desc: Use image node_id 4 for calling the LinearModel obj's underlying member method code with example usage: obj._set_intercept(X_offset, y_offset, X_scale) without return types