def dihedral(n):
    if n == 1:
        yield Permutation([0, 1])
        yield Permutation([1, 0])
    elif n == 2:
        yield Permutation([0, 1, 2, 3])
        yield Permutation([1, 0, 3, 2])
        yield Permutation([2, 3, 0, 1])
        yield Permutation([3, 2, 1, 0])
    else:
        gen = list(range(n))
        for i in range(n):
            yield Permutation(gen)
            yield Permutation(gen[::-1])
            gen = rotate_left(gen, 1)
Creates the computational graph for the reconstruction job.
dataset/ETHPy150Open sympy/sympy/sympy/combinatorics/generators.py/dihedral--crossover with--dataset/ETHPy150Open blackecho/Deep-Learning-TensorFlow/models/autoencoder_models/stacked_denoising_autoencoder.py/StackedDenoisingAutoencoder.build_reconstruction_model