def binomial(shape, counts, probabilities, dtype=None, seed=None):
    """Draw samples from a Binomial distribution.

    The values are drawn from a Binomial distribution with
    specified trial count and probability of success.

    Args:
        shape: The shape of the random values to generate.
        counts: A number or array of numbers representing the
            number of trials. It must be broadcastable with `probabilities`.
        probabilities: A float or array of floats representing the
            probability of success of an individual event.
            It must be broadcastable with `counts`.
        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: Optional Python integer or instance of
           `keras.random.SeedGenerator`.
            By default, the `seed` argument is `None`, and an internal global
            `keras.random.SeedGenerator` is used. The `seed` argument can be
            used to ensure deterministic (repeatable) random number generation.
            Note that passing an integer as the `seed` value will produce the
            same random values for each call. To generate different random
            values for repeated calls, an instance of
            `keras.random.SeedGenerator` must be provided as the `seed` value.
            Remark concerning the JAX backend: When tracing functions with the
            JAX backend the global `keras.random.SeedGenerator` is not
            supported. Therefore, during tracing the default value seed=None
            will produce an error, and a `seed` argument must be provided.
    """
    return backend.random.binomial(
        shape,
        counts=counts,
        probabilities=probabilities,
        dtype=dtype,
        seed=seed,
    )
Draw samples from a Binomial distribution.

    The values are drawn from a Binomial distribution with
    specified trial count and probability of success.

    Args:
        shape: The shape of the random values to generate.
        counts: A number or array of numbers representing the
            number of trials. It must be broadcastable with `probabilities`.
        probabilities: A float or array of floats representing the
            probability of success of an individual event.
            It must be broadcastable with `counts`.
        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: Optional Python integer or instance of
           `keras.random.SeedGenerator`.
            By default, the `seed` argument is `None`, and an internal global
            `keras.random.SeedGenerator` is used. The `seed` argument can be
            used to ensure deterministic (repeatable) random number generation.
            Note that passing an integer as the `seed` value will produce the
            same random values for each call. To generate different random
            values for repeated calls, an instance of
            `keras.random.SeedGenerator` must be provided as the `seed` value.
            Remark concerning the JAX backend: When tracing functions with the
            JAX backend the global `keras.random.SeedGenerator` is not
            supported. Therefore, during tracing the default value seed=None
            will produce an error, and a `seed` argument must be provided.
    
keras/src/random/random.py