code: def _random_binomial_cpu(
      shape,
      counts,
      probs,
      output_dtype=tf.float32,
      seed=None,
      name=None,
  ):
      """Sample using *fast* `tf.random.stateless_binomial`."""
      with tf.name_scope(name or "binomial_cpu"):
          probs = tf.where(counts > 0, probs, 0)
          batch_shape = ps.broadcast_shape(
              ps.shape(counts), ps.shape(probs)
          )
          samples = tf.random.stateless_binomial(
              shape=ps.concat([shape, batch_shape], axis=0),
              seed=seed,
              counts=counts,
              probs=probs,
              output_dtype=output_dtype,
          )
      return samples
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import batched_rejection_sampler
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import implementation_selection
  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 import special
label_desc: Use image node_id 2 for calling a global function with example usage: _random_binomial_cpu(shape, counts, probs, output_dtype, seed, name) and returns: samples