code: def categorical(logits, num_samples, dtype="int64", seed=None):
      seed = tf_draw_seed(seed)
      output = tf.random.stateless_categorical(
          logits, num_samples, seed=seed
      )
      return tf.cast(output, dtype)
  
file_path: keras/keras/backend/tensorflow/random.py
imports: import tensorflow
  from tensorflow.experimental import numpy
  from keras.backend.common import standardize_dtype
  from keras.backend.config import floatx
  from keras.random.seed_generator import SeedGenerator
  from keras.random.seed_generator import draw_seed
  from keras.random.seed_generator import make_default_seed
label_desc: Use image node_id 4 for calling a global function with example usage: categorical(logits, num_samples, dtype, seed) and returns: tf