def __init__(self, shape, min_value, max_value, alpha=<NUM_LIT:0.0>, beta=<NUM_LIT:0.0>, scope='<STR_LIT>', summary_labels=()):
assert min_value is None or max_value > min_value<EOL>self.shape = shape<EOL>self.min_value = min_value<EOL>self.max_value = max_value<EOL>action_size = util.prod(self.shape)<EOL>self.alpha = Linear(size=action_size, bias=alpha, scope='<STR_LIT>', summary_labels=summary_labels)<EOL>self.beta = Linear(size=action_size, bias=beta, scope='<STR_LIT>', summary_labels=summary_labels)<EOL>super(Beta, self).__init__(shape=shape, scope=scope, summary_labels=summary_labels)<EOL>
Beta distribution.

Args:
    shape: Action shape.
    min_value: Minimum value of continuous actions.
    max_value: Maximum value of continuous actions.
    alpha: Optional distribution bias for the alpha value.
    beta: Optional distribution bias for the beta value.