code: def _von_mises_sample_with_gradient(shape, concentration, seed):
      """Performs rejection sampling for standardized von Mises.
  
      Args:
        shape: The output sample shape.
        concentration: The concentration parameter of the distribution.
        seed: PRNG seed; see `tfp.random.sanitize_seed` for details.
  
      Returns:
        sample: Differentiable samples of standardized von Mises.
      """
      return _von_mises_sample_no_gradient(shape, concentration, seed)
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import custom_gradient
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.math.gradient import value_and_gradient
label_desc: Use image node_id 10 for calling a global function with example usage: _von_mises_sample_with_gradient(shape, concentration, seed) and returns: _von_mises_sample_no_gradient