def __init__(self, learning_rate, num_samples=<NUM_LIT:1>, unroll_loop=False, scope='<STR_LIT>', summary_labels=()):
assert isinstance(learning_rate, float) and learning_rate > <NUM_LIT:0.0><EOL>self.learning_rate = learning_rate<EOL>assert isinstance(num_samples, int) and num_samples > <NUM_LIT:0><EOL>self.num_samples = num_samples<EOL>assert isinstance(unroll_loop, bool)<EOL>self.unroll_loop = unroll_loop<EOL>super(Evolutionary, self).__init__(scope=scope, summary_labels=summary_labels)<EOL>
Creates a new evolutionary optimizer instance.

Args:
    learning_rate: Learning rate.
    num_samples: Number of sampled perturbations.