code: def _von_mises_sample_jvp(shape, primals, tangents):
      """Compute primals and tangents using implicit derivative."""
      concentration, seed = primals
      dconcentration, dseed = tangents
      del dseed
  
      dconcentration = tf.broadcast_to(dconcentration, shape)
      broadcast_concentration = tf.broadcast_to(concentration, shape)
  
      samples = _von_mises_sample_no_gradient(
          shape, concentration, seed
      )
  
      _, dcdf_dconcentration = value_and_gradient(
          lambda conc: von_mises_cdf(samples, conc),
          broadcast_concentration,
      )
      inv_prob = tf.exp(-concentration * cosxm1(samples)) * (
          (2.0 * np.pi) * tf.math.bessel_i0e(concentration)
      )
      # Compute the implicit derivative,
      #   dz = dconc * -(dF(z; conc) / dconc) / p(z; conc)
      dsamples = dconcentration * (-dcdf_dconcentration * inv_prob)
  
      return samples, dsamples
  
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 9 for calling a global function with example usage: _von_mises_sample_jvp(shape, primals, tangents) and returns: samples,  dsamples