code: def test_coerce_to_dict_with_list_of_arrays():
      test_input = [np.array([0, 0, 1]), np.array([0, 1, 0])]
      expected_output = {
          "[0 0 1]": np.array([0, 0, 1]),
          "[0 1 0]": np.array([0, 1, 0]),
      }
      actual_output = glmr._coerce_to_dict(test_input)
      assert actual_output.keys() == expected_output.keys()
      for key in actual_output:
          assert np.array_equal(
              actual_output[key], expected_output[key]
          )
  
imports: import numpy
  import pandas
  import pytest
  from nibabel import load
  from nilearn._utils.data_gen import write_fake_fmri_data_and_design
  from nilearn.glm.first_level import FirstLevelModel
  from nilearn.glm.first_level.design_matrix import make_first_level_design_matrix
  from nilearn.glm.second_level import SecondLevelModel
  from nilearn.maskers import NiftiMasker
  from nilearn.reporting import glm_reporter
label_desc: Use image node_id 12 for calling a global function with example usage: test_coerce_to_dict_with_list_of_arrays() without return types