code: def _is_approx_wolfe(_, f_x, df_x, f_0, df_0, delta, sigma, epsilon):
      flim = f_0 + epsilon * np.abs(f_0)
      decrease_cond = (2 * delta - 1) * df_0 >= df_x
      curvature_cond = df_x >= sigma * df_0
      return (f_x <= flim) & decrease_cond & curvature_cond
  
imports: import collections
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.optimizer.linesearch.hager_zhang import hager_zhang
label_desc: Use image node_id 2 for calling a global function with example usage: _is_approx_wolfe(_, f_x, df_x, f_0, df_0, delta, sigma, epsilon) and returns: unknown