code: def _solve_svd(X, y, alpha):
      U, s, Vt = linalg.svd(X, full_matrices=False)
      idx = s > 1e-15  # same default value as scipy.linalg.pinv
      s_nnz = s[idx][:, np.newaxis]
      UTy = np.dot(U.T, y)
      d = np.zeros((s.size, alpha.size))
      d[idx] = s_nnz / (s_nnz**2 + alpha)
      d_UT_y = d * UTy
      return np.dot(Vt.T, d_UT_y).T
  
imports: from abc import ABCMeta, abstractmethod
  import warnings
  import numpy
  from scipy import linalg
  from scipy import sparse
  from scipy.sparse import linalg
  from .base import LinearClassifierMixin, LinearModel, _rescale_data
  from .sag import sag_solver
  from ..base import RegressorMixin
  from ..utils.extmath import safe_sparse_dot
  from ..utils.extmath import row_norms
  from ..utils import check_X_y
  from ..utils import check_array
  from ..utils import check_consistent_length
  from ..utils import compute_sample_weight
  from ..utils import column_or_1d
  from ..preprocessing import LabelBinarizer
  from ..model_selection import GridSearchCV
  from ..externals import six
  from ..metrics.scorer import check_scoring
label_desc: Use image node_id 5 for calling a global function with example usage: _solve_svd(X, y, alpha) and returns: np