code: def test_softmax_fixtures():
      assert_allclose(
          softmax([1000, 0, 0, 0]), np.array([1, 0, 0, 0]), rtol=1e-13
      )
      assert_allclose(softmax([1, 1]), np.array([0.5, 0.5]), rtol=1e-13)
      assert_allclose(
          softmax([0, 1]), np.array([1, np.e]) / (1 + np.e), rtol=1e-13
      )
  
      # Expected value computed using mpmath (with mpmath.mp.dps = 200) and then
      # converted to float.
      x = np.arange(4)
      expected = np.array(
          [
              0.03205860328008499,
              0.08714431874203256,
              0.23688281808991013,
              0.6439142598879722,
          ]
      )
  
      assert_allclose(softmax(x), expected, rtol=1e-13)
  
      # Translation property.  If all the values are changed by the same amount,
      # the softmax result does not change.
      assert_allclose(softmax(x + 100), expected, rtol=1e-13)
  
      # When axis=None, softmax operates on the entire array, and preserves
      # the shape.
      assert_allclose(
          softmax(x.reshape(2, 2)), expected.reshape(2, 2), rtol=1e-13
      )
  
file_path: scipy/scipy/special/tests/test_logsumexp.py
imports: import numpy
  from numpy.testing import assert_almost_equal, assert_equal, assert_allclose, assert_array_almost_equal, assert_
  from scipy.special import logsumexp, softmax
label_desc: Use image node_id 10 for calling a global function with example usage: test_softmax_fixtures() without return types