code: def categorical(logits, num_samples, dtype="int32", seed=None):
      """Draws samples from a categorical distribution.
  
      This function takes as input `logits`, a 2-D input tensor with shape
      (batch_size, num_classes). Each row of the input represents a categorical
      distribution, with each column index containing the log-probability for a
      given class.
  
      The function will output a 2-D tensor with shape (batch_size, num_samples),
      where each row contains samples from the corresponding row in `logits`.
      Each column index contains an independent samples drawn from the input
      distribution.
  
      Args:
          logits: 2-D Tensor with shape (batch_size, num_classes). Each row
              should define a categorical distibution with the unnormalized
              log-probabilities for all classes.
          num_samples: Int, the number of independent samples to draw for each
              row of the input. This will be the second dimension of the output
              tensor's shape.
          dtype: Optional dtype of the output tensor.
          seed: A Python integer or instance of
              `keras.random.SeedGenerator`.
              Used to make the behavior of the initializer
              deterministic. Note that an initializer seeded with an integer
              or None (unseeded) will produce the same random values
              across multiple calls. To get different random values
              across multiple calls, use as seed an instance
              of `keras.random.SeedGenerator`.
  
      Returns:
          A 2-D tensor with (batch_size, num_samples).
      """
      logits_shape = list(backend.convert_to_tensor(logits).shape)
      if len(logits_shape) != 2:
          raise ValueError(
              "`logits` should be a 2-D tensor with shape "
              f"[batch_size, num_classes]. Received: logits={logits}"
          )
      return backend.random.categorical(
          logits, num_samples, dtype=dtype, seed=seed
      )
  
file_path: keras/keras/random/random.py
imports: from keras import backend
  from keras.api_export import keras_export
label_desc: Use image node_id 2 for calling a global function with example usage: categorical(logits, num_samples, dtype, seed) and returns: backend