code: def test_max_iter():
      def ricker_function(resolution, center, width):
          """Discrete sub-sampled Ricker (Mexican hat) wavelet"""
          x = np.linspace(0, resolution - 1, resolution)
          x = (
              (2 / (np.sqrt(3 * width) * np.pi**0.25))
              * (1 - (x - center) ** 2 / width**2)
              * np.exp(-((x - center) ** 2) / (2 * width**2))
          )
          return x
  
      def ricker_matrix(width, resolution, n_components):
          """Dictionary of Ricker (Mexican hat) wavelets"""
          centers = np.linspace(0, resolution - 1, n_components)
          D = np.empty((n_components, resolution))
          for i, center in enumerate(centers):
              D[i] = ricker_function(resolution, center, width)
          D /= np.sqrt(np.sum(D**2, axis=1))[:, np.newaxis]
          return D
  
      transform_algorithm = "lasso_cd"
      resolution = 1024
      subsampling = 3  # subsampling factor
      n_components = resolution // subsampling
  
      # Compute a wavelet dictionary
      D_multi = np.r_[
          tuple(
              ricker_matrix(
                  width=w,
                  resolution=resolution,
                  n_components=n_components // 5,
              )
              for w in (10, 50, 100, 500, 1000)
          )
      ]
  
      X = np.linspace(0, resolution - 1, resolution)
      first_quarter = X < resolution / 4
      X[first_quarter] = 3.0
      X[np.logical_not(first_quarter)] = -1.0
      X = X.reshape(1, -1)
  
      # check that the underlying model fails to converge
      with pytest.warns(ConvergenceWarning):
          model = SparseCoder(
              D_multi,
              transform_algorithm=transform_algorithm,
              transform_max_iter=1,
          )
          model.fit_transform(X)
  
      # check that the underlying model converges w/o warnings
      with warnings.catch_warnings():
          warnings.simplefilter("error", ConvergenceWarning)
          model = SparseCoder(
              D_multi,
              transform_algorithm=transform_algorithm,
              transform_max_iter=2000,
          )
          model.fit_transform(X)
  
imports: import itertools
  import warnings
  from functools import partial
  import numpy
  import pytest
  import sklearn
  from sklearn.base import clone
  from sklearn.decomposition import DictionaryLearning, MiniBatchDictionaryLearning, SparseCoder, dict_learning, dict_learning_online, sparse_encode
  from sklearn.decomposition._dict_learning import _update_dict
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.utils import check_array
  from sklearn.utils._testing import TempMemmap, assert_allclose, assert_array_almost_equal, assert_array_equal, ignore_warnings
  from sklearn.utils.estimator_checks import check_transformer_data_not_an_array, check_transformer_general, check_transformers_unfitted
  from sklearn.utils.parallel import Parallel
label_desc: Use image node_id 4 for calling a global function with example usage: test_max_iter() and returns: x, D