code: def lasso_path(
      X,
      y,
      eps=1e-3,
      n_alphas=100,
      alphas=None,
      precompute="auto",
      Xy=None,
      copy_X=True,
      coef_init=None,
      verbose=False,
      return_n_iter=False,
      positive=False,
      **params,
  ):
      """Compute Lasso path with coordinate descent
  
      The Lasso optimization function varies for mono and multi-outputs.
  
      For mono-output tasks it is::
  
          (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
  
      For multi-output tasks it is::
  
          (1 / (2 * n_samples)) * ||Y - XW||^2_Fro + alpha * ||W||_21
  
      Where::
  
          ||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
  
      i.e. the sum of norm of each row.
  
      Read more in the :ref:`User Guide <lasso>`.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape (n_samples, n_features)
          Training data. Pass directly as Fortran-contiguous data to avoid
          unnecessary memory duplication. If ``y`` is mono-output then ``X``
          can be sparse.
  
      y : ndarray, shape (n_samples,), or (n_samples, n_outputs)
          Target values
  
      eps : float, optional
          Length of the path. ``eps=1e-3`` means that
          ``alpha_min / alpha_max = 1e-3``
  
      n_alphas : int, optional
          Number of alphas along the regularization path
  
      alphas : ndarray, optional
          List of alphas where to compute the models.
          If ``None`` alphas are set automatically
  
      precompute : True | False | 'auto' | array-like
          Whether to use a precomputed Gram matrix to speed up
          calculations. If set to ``'auto'`` let us decide. The Gram
          matrix can also be passed as argument.
  
      Xy : array-like, optional
          Xy = np.dot(X.T, y) that can be precomputed. It is useful
          only when the Gram matrix is precomputed.
  
      copy_X : boolean, optional, default True
          If ``True``, X will be copied; else, it may be overwritten.
  
      coef_init : array, shape (n_features, ) | None
          The initial values of the coefficients.
  
      verbose : bool or integer
          Amount of verbosity.
  
      params : kwargs
          keyword arguments passed to the coordinate descent solver.
  
      positive : bool, default False
          If set to True, forces coefficients to be positive.
  
      return_n_iter : bool
          whether to return the number of iterations or not.
  
      Returns
      -------
      alphas : array, shape (n_alphas,)
          The alphas along the path where models are computed.
  
      coefs : array, shape (n_features, n_alphas) or \
              (n_outputs, n_features, n_alphas)
          Coefficients along the path.
  
      dual_gaps : array, shape (n_alphas,)
          The dual gaps at the end of the optimization for each alpha.
  
      n_iters : array-like, shape (n_alphas,)
          The number of iterations taken by the coordinate descent optimizer to
          reach the specified tolerance for each alpha.
  
      Notes
      -----
      See examples/linear_model/plot_lasso_coordinate_descent_path.py
      for an example.
  
      To avoid unnecessary memory duplication the X argument of the fit method
      should be directly passed as a Fortran-contiguous numpy array.
  
      Note that in certain cases, the Lars solver may be significantly
      faster to implement this functionality. In particular, linear
      interpolation can be used to retrieve model coefficients between the
      values output by lars_path
  
      Examples
      ---------
  
      Comparing lasso_path and lars_path with interpolation:
  
      >>> X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
      >>> y = np.array([1, 2, 3.1])
      >>> # Use lasso_path to compute a coefficient path
      >>> _, coef_path, _ = lasso_path(X, y, alphas=[5., 1., .5])
      >>> print(coef_path)
      [[ 0.          0.          0.46874778]
       [ 0.2159048   0.4425765   0.23689075]]
  
      >>> # Now use lars_path and 1D linear interpolation to compute the
      >>> # same path
      >>> from sklearn.linear_model import lars_path
      >>> alphas, active, coef_path_lars = lars_path(X, y, method='lasso')
      >>> from scipy import interpolate
      >>> coef_path_continuous = interpolate.interp1d(alphas[::-1],
      ...                                             coef_path_lars[:, ::-1])
      >>> print(coef_path_continuous([5., 1., .5]))
      [[ 0.          0.          0.46915237]
       [ 0.2159048   0.4425765   0.23668876]]
  
  
      See also
      --------
      lars_path
      Lasso
      LassoLars
      LassoCV
      LassoLarsCV
      sklearn.decomposition.sparse_encode
      """
      return enet_path(
          X,
          y,
          l1_ratio=1.0,
          eps=eps,
          n_alphas=n_alphas,
          alphas=alphas,
          precompute=precompute,
          Xy=Xy,
          copy_X=copy_X,
          coef_init=coef_init,
          verbose=verbose,
          positive=positive,
          return_n_iter=return_n_iter,
          **params,
      )
  
imports: import sys
  import warnings
  from abc import ABCMeta, abstractmethod
  import numpy
  from scipy import sparse
  from .base import LinearModel, _pre_fit
  from ..base import RegressorMixin
  from .base import _preprocess_data
  from ..utils import check_array, check_X_y, deprecated
  from ..utils.validation import check_random_state
  from ..model_selection import check_cv
  from ..externals.joblib import Parallel, delayed
  from ..externals import six
  from ..externals.six.moves import xrange
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.validation import column_or_1d
  from ..exceptions import ConvergenceWarning
  from .None import cd_fast
label_desc: Use image node_id 2 for calling a global function with example usage: lasso_path(X, y, eps, n_alphas, alphas, precompute, Xy, copy_X, coef_init, verbose, return_n_iter, positive) and returns: enet_path