keras/src/random/random.py/uniform
def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
    """Draw samples from a uniform distribution.

    The generated values follow a uniform distribution in the range
    `[minval, maxval)`. The lower bound `minval` is included in the range,
    while the upper bound `maxval` is excluded.

    `dtype` must be a floating point type, the default range is `[0, 1)`.

    Args:
        shape: The shape of the random values to generate.
        minval: Float, defaults to 0. Lower bound of the range of
            random values to generate (inclusive).
        maxval: Float, defaults to 1. Upper bound of the range of
            random values to generate (exclusive).
        dtype: Optional dtype of the tensor. Only floating point types are
            supported. If not specified, `keras.config.floatx()` is used,
            which defaults to `float32` unless you configured it otherwise (via
            `keras.config.set_floatx(float_dtype)`)
        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`.
    """
    if dtype and not backend.is_float_dtype(dtype):
        raise ValueError(
            "`keras.random.uniform` requires a floating point `dtype`. "
            f"Received: dtype={dtype} "
        )
    return backend.random.uniform(
        shape, minval=minval, maxval=maxval, dtype=dtype, seed=seed
    )