code: def test_lars_dtype_match(LARS, has_coef_path, args, dtype):
      # The test ensures that the fit method preserves input dtype
      rng = np.random.RandomState(0)
      X = rng.rand(20, 6).astype(dtype)
      y = rng.rand(20).astype(dtype)
  
      model = LARS(**args)
      model.fit(X, y)
      assert model.coef_.dtype == dtype
      if has_coef_path:
          assert model.coef_path_.dtype == dtype
      assert model.intercept_.dtype == dtype
  
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 37 for calling a global function with example usage: test_lars_dtype_match(LARS, has_coef_path, args, dtype) without return types