code: def test_lars_numeric_consistency(LARS, has_coef_path, args):
      # The test ensures numerical consistency between trained coefficients
      # of float32 and float64.
      rtol = 1e-5
      atol = 1e-5
  
      rng = np.random.RandomState(0)
      X_64 = rng.rand(10, 6)
      y_64 = rng.rand(10)
  
      model_64 = LARS(**args).fit(X_64, y_64)
      model_32 = LARS(**args).fit(
          X_64.astype(np.float32), y_64.astype(np.float32)
      )
  
      assert_allclose(
          model_64.coef_, model_32.coef_, rtol=rtol, atol=atol
      )
      if has_coef_path:
          assert_allclose(
              model_64.coef_path_,
              model_32.coef_path_,
              rtol=rtol,
              atol=atol,
          )
      assert_allclose(
          model_64.intercept_, model_32.intercept_, rtol=rtol, atol=atol
      )
  
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 38 for calling a global function with example usage: test_lars_numeric_consistency(LARS, has_coef_path, args) without return types