code: def _random_gamma_gradient(
      shape, concentration, rate, log_rate, seed, log_space
  ):
      return _random_gamma_no_gradient(
          shape, concentration, rate, log_rate, seed, 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 11 for calling a global function with example usage: _random_gamma_gradient(shape, concentration, rate, log_rate, seed, log_space) and returns: _random_gamma_no_gradient