code: def _check_convergence(
      simplex,
      best_vertex,
      best_objective,
      worst_objective,
      func_tolerance,
      position_tolerance,
  ):
      """Returns True if the simplex has converged.
  
      If the simplex size is smaller than the `position_tolerance` or the variation
      of the function value over the vertices of the simplex is smaller than the
      `func_tolerance` return True else False.
  
      Args:
        simplex: `Tensor` of real dtype. The simplex to test for convergence. For
          more details, see the docstring for `initial_simplex` argument
          of `minimize`.
        best_vertex: `Tensor` of real dtype and rank one less than `simplex`. The
          vertex with the best (i.e. smallest) objective value.
        best_objective: Scalar `Tensor` of real dtype. The best (i.e. smallest)
          value of the objective function at a vertex.
        worst_objective: Scalar `Tensor` of same dtype as `best_objective`. The
          worst (i.e. largest) value of the objective function at a vertex.
        func_tolerance: Scalar positive `Tensor`. The tolerance for the variation
          of the objective function value over the simplex. If the variation over
          the simplex vertices is below this threshold, convergence is True.
        position_tolerance: Scalar positive `Tensor`. The algorithm stops if the
          lengths (under the supremum norm) of edges connecting to the best vertex
          are below this threshold.
  
      Returns:
        has_converged: A scalar boolean `Tensor` indicating whether the algorithm
          is deemed to have converged.
      """
      objective_convergence = (
          tf.abs(worst_objective - best_objective) < func_tolerance
      )
      simplex_degeneracy = (
          tf.reduce_max(tf.abs(simplex - best_vertex))
          < position_tolerance
      )
      return objective_convergence | simplex_degeneracy
  
imports: import collections
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 9 for calling a global function with example usage: _check_convergence(simplex, best_vertex, best_objective, worst_objective, func_tolerance, position_tolerance) and returns: unknown