code: def beta(shape, alpha, beta, dtype=None, seed=None):
      """Draw samples from a Beta distribution.
  
      The values are drawm from a Beta distribution parametrized
      by alpha and beta.
  
      Args:
          shape: The shape of the random values to generate.
          alpha: Float or an array of floats representing the first
              parameter alpha. Must be broadcastable with `beta` and `shape`.
          beta: Float or an array of floats representing the second
              parameter beta. Must be broadcastable with `alpha` and `shape`.
          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`.
      """
      return backend.random.beta(
          shape=shape, alpha=alpha, beta=beta, 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 10 for calling a global function with example usage: beta(shape, alpha, beta, dtype, seed) and returns: backend