docstr: Tests that bisect works in batching mode.
docstr_tok: ["Tests","that","bisect","works","in","batching","mode","."]
code: def test_bisect_batching(self):
      """Tests that bisect works in batching mode."""
      wolfe_threshold = 1e-6
      # Let's build our example function with 4 batches, each evaluating a
      # different poly. They all have negative slopes both on 0.0 and 1.0,
      # but different slopes (positive, negative) and values (low enough, too
      # high) on their midpoint.
      x = np.array([0.0, 0.5, 1.0])
      y = np.array(
          [
              [1.0, 0.6, 1.2],
              [1.0, 0.6, 1.2],
              [1.0, 1.6, 1.2],
              [1.0, 1.6, 1.2],
          ]
      )
      dy = np.array(
          [
              [-0.8, 0.6, -0.7],
              [-0.8, -0.4, -0.7],
              [-0.8, 0.8, -0.7],
              [-0.8, -0.4, -0.7],
          ]
      )
      fun = _test_function_x_y_dy(x, y, dy)
  
      val_a = fun(0.0)  # Values at zero.
      val_b = fun(1.0)  # Values at initial step.
      f_lim = val_a.f + (wolfe_threshold * tf.abs(val_a.f))
  
      expected_left = np.array([0.0, 0.5, 0.0, 0.0])
      expected_right = np.array([0.5, 0.75, 0.5, 0.25])
  
      result = self.evaluate(hzl.bisect(fun, val_a, val_b, f_lim))
      self.assertTrue(np.all(result.stopped))
      self.assertTrue(np.all(~result.failed))
      self.assertTrue(np.all(result.left.df < 0))
      self.assertTrue(np.all(result.right.df >= 0))
      self.assertArrayNear(result.left.x, expected_left, 1e-5)
      self.assertArrayNear(result.right.x, expected_right, 1e-5)
  
imports: import collections
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.gradient import value_and_gradient
  from tensorflow_probability.python.optimizer.linesearch.internal import hager_zhang_lib
label_desc: Use image node_id 9 for calling the HagerZhangLibTest obj's underlying member method code with example usage: obj.test_bisect_batching() without return types