code: def _random_gamma_no_gradient(
      shape, concentration, rate, log_rate, seed, log_space
  ):
      """Sample a gamma, CPU specialized to stateless_gamma.
  
      Args:
        shape: Sample shape.
        concentration: Concentration of gamma distribution.
        rate: Rate parameter of gamma distribution.
        log_rate: Log-rate parameter of gamma distribution.
        seed: PRNG seed; see `tfp.random.sanitize_seed` for details.
        log_space: If `True`, draw log-of-gamma samples.
  
      Returns:
        samples: Samples from gamma distributions.
      """
      seed = samplers.sanitize_seed(seed)
  
      sampler_impl = implementation_selection.implementation_selecting(
          fn_name="gamma",
          default_fn=_random_gamma_noncpu,
          cpu_fn=_random_gamma_cpu,
      )
      return sampler_impl(
          shape=shape,
          concentration=concentration,
          rate=rate,
          log_rate=log_rate,
          seed=seed,
          log_space=log_space,
      )
  
imports: import numpy
  import tensorflow.compat.v2
  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.internal import assert_util
  from tensorflow_probability.python.internal import batched_rejection_sampler
  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 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 generic
  from tensorflow_probability.python.math import special
  from tensorflow_probability.python.util.deferred_tensor import DeferredTensor
label_desc: Use image node_id 6 for calling a global function with example usage: _random_gamma_no_gradient(shape, concentration, rate, log_rate, seed, log_space) and returns: sampler_impl