keras/src/random/random.py/categorical
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
    )