code: def _compute_num_states_batched(next_log_joint, log_slice_sample):
      # Returns the number of states (of necessity, at most one per batch member)
      # represented by the `next_log_joint` Tensor that are good enough to pass the
      # slice variable.
      with tf.name_scope("compute_num_states_batched"):
          return tf.cast(
              next_log_joint > log_slice_sample, dtype=tf.int64
          )
  
imports: import collections
  import functools
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python import random
  from tensorflow_probability.python.experimental import auto_batching
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.mcmc import kernel
  from tensorflow_probability.python.util.seed_stream import SeedStream
label_desc: Use image node_id 10 for calling a global function with example usage: _compute_num_states_batched(next_log_joint, log_slice_sample) and returns: tf