code: def make_relaxed_categorical(
      batch_shape, num_classes, dtype=tf.float32
  ):
      seed_stream = test_util.test_seed_stream("relaxed_categorical")
      logits = -50.0 + tf.random.uniform(
          list(batch_shape) + [num_classes],
          -10,
          10,
          dtype=dtype,
          seed=seed_stream(),
      )
      temperatures = tf.random.uniform(
          list(batch_shape),
          0.1,
          10,
          dtype=tf.float32,
          seed=seed_stream(),
      )
      return roc.RelaxedOneHotCategorical(
          temperatures, logits, validate_args=True
      )
  
imports: from absl.testing import parameterized
  import numpy
  from scipy.special import gamma
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import relaxed_onehot_categorical
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: make_relaxed_categorical(batch_shape, num_classes, dtype) and returns: roc