def generator(ngf, nc, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12, z_dim=100, activation='sigmoid'):
    '''The genrator is a CNN which takes 100 dimensional embedding as input
    and reconstructs the input image given to the encoder
    '''
    BatchNorm = mx.sym.BatchNorm
    rand = mx.sym.Variable('rand')

    rand = mx.sym.Reshape(rand, shape=(-1, z_dim, 1, 1))

    g1 = mx.sym.Deconvolution(rand, name='gen1', kernel=(5,5), stride=(2,2),target_shape=(2,2), num_filter=ngf*8, no_bias=no_bias)
    gbn1 = BatchNorm(g1, name='genbn1', fix_gamma=fix_gamma, eps=eps)
    gact1 = mx.sym.Activation(gbn1, name="genact1", act_type="relu")

    g2 = mx.sym.Deconvolution(gact1, name='gen2', kernel=(5,5), stride=(2,2),target_shape=(4,4), num_filter=ngf*4, no_bias=no_bias)
    gbn2 = BatchNorm(g2, name='genbn2', fix_gamma=fix_gamma, eps=eps)
    gact2 = mx.sym.Activation(gbn2, name='genact2', act_type='relu')

    g3 = mx.sym.Deconvolution(gact2, name='gen3', kernel=(5,5), stride=(2,2), target_shape=(8,8), num_filter=ngf*2, no_bias=no_bias)
    gbn3 = BatchNorm(g3, name='genbn3', fix_gamma=fix_gamma, eps=eps)
    gact3 = mx.sym.Activation(gbn3, name='genact3', act_type='relu')

    g4 = mx.sym.Deconvolution(gact3, name='gen4', kernel=(5,5), stride=(2,2), target_shape=(16,16), num_filter=ngf, no_bias=no_bias)
    gbn4 = BatchNorm(g4, name='genbn4', fix_gamma=fix_gamma, eps=eps)
    gact4 = mx.sym.Activation(gbn4, name='genact4', act_type='relu')

    g5 = mx.sym.Deconvolution(gact4, name='gen5', kernel=(5,5), stride=(2,2), target_shape=(32,32), num_filter=nc, no_bias=no_bias)
    gout = mx.sym.Activation(g5, name='genact5', act_type=activation)

    return gout
The genrator is a CNN which takes 100 dimensional embedding as input
    and reconstructs the input image given to the encoder