code: def test_estimatorclasses_positive_constraint():
      # testing the transmissibility for the positive option of all estimator
      # classes in this same function here
      default_parameter = {"fit_intercept": False}
  
      estimator_parameter_map = {
          "LassoLars": {"alpha": 0.1},
          "LassoLarsCV": {},
          "LassoLarsIC": {},
      }
      for estname in estimator_parameter_map:
          params = default_parameter.copy()
          params.update(estimator_parameter_map[estname])
          estimator = getattr(linear_model, estname)(
              positive=False, **params
          )
          estimator.fit(X, y)
          assert estimator.coef_.min() < 0
          estimator = getattr(linear_model, estname)(
              positive=True, **params
          )
          estimator.fit(X, y)
          assert min(estimator.coef_) >= 0
  
imports: import warnings
  import numpy
  import pytest
  from scipy import linalg
  from sklearn import datasets, linear_model
  from sklearn.base import clone
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model import Lars, LarsCV, LassoLars, LassoLarsCV, LassoLarsIC, lars_path
  from sklearn.linear_model._least_angle import _lars_path_residues
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils._testing import TempMemmap, assert_allclose, assert_array_almost_equal, ignore_warnings
label_desc: Use image node_id 29 for calling a global function with example usage: test_estimatorclasses_positive_constraint() without return types