import chainer
import chainer.links as L
from chainer.initializers import HeNormal


class Parameters(chainer.Chain):

    def __init__(self, channels_chz):
        super().__init__(lstm_tanh=L.Convolution2D(None, channels_chz,
            ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)), lstm_i=L.
            Convolution2D(None, channels_chz, ksize=5, stride=1, pad=2,
            initialW=HeNormal(0.1)), lstm_f=L.Convolution2D(None,
            channels_chz, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            lstm_o=L.Convolution2D(None, channels_chz, ksize=5, stride=1,
            pad=2, initialW=HeNormal(0.1)), mean_z=L.Convolution2D(None,
            channels_chz, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            conv_x_1=L.Convolution2D(None, channels_chz, ksize=2, stride=2,
            pad=0, initialW=HeNormal(0.1)), conv_x_2=L.Convolution2D(None,
            channels_chz, ksize=2, stride=2, pad=0, initialW=HeNormal(0.1)),
            ln_var_z=L.Convolution2D(None, channels_chz, ksize=5, stride=1,
            pad=2, initialW=HeNormal(0.1)))

68892807f0c6f7ba95ca418c6812b7380333950b
<mask token>

<mask token>


class Parameters(chainer.Chain):
    <mask token>

<mask token>


class Parameters(chainer.Chain):

    def __init__(self, channels_chz):
        super().__init__(lstm_tanh=L.Convolution2D(None, channels_chz,
            ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)), lstm_i=L.
            Convolution2D(None, channels_chz, ksize=5, stride=1, pad=2,
            initialW=HeNormal(0.1)), lstm_f=L.Convolution2D(None,
            channels_chz, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            lstm_o=L.Convolution2D(None, channels_chz, ksize=5, stride=1,
            pad=2, initialW=HeNormal(0.1)), mean_z=L.Convolution2D(None,
            channels_chz, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            conv_x_1=L.Convolution2D(None, channels_chz, ksize=2, stride=2,
            pad=0, initialW=HeNormal(0.1)), conv_x_2=L.Convolution2D(None,
            channels_chz, ksize=2, stride=2, pad=0, initialW=HeNormal(0.1)),
            ln_var_z=L.Convolution2D(None, channels_chz, ksize=5, stride=1,
            pad=2, initialW=HeNormal(0.1)))

import chainer
import chainer.links as L
from chainer.initializers import HeNormal


class Parameters(chainer.Chain):

    def __init__(self, channels_chz):
        super().__init__(lstm_tanh=L.Convolution2D(None, channels_chz,
            ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)), lstm_i=L.
            Convolution2D(None, channels_chz, ksize=5, stride=1, pad=2,
            initialW=HeNormal(0.1)), lstm_f=L.Convolution2D(None,
            channels_chz, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            lstm_o=L.Convolution2D(None, channels_chz, ksize=5, stride=1,
            pad=2, initialW=HeNormal(0.1)), mean_z=L.Convolution2D(None,
            channels_chz, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            conv_x_1=L.Convolution2D(None, channels_chz, ksize=2, stride=2,
            pad=0, initialW=HeNormal(0.1)), conv_x_2=L.Convolution2D(None,
            channels_chz, ksize=2, stride=2, pad=0, initialW=HeNormal(0.1)),
            ln_var_z=L.Convolution2D(None, channels_chz, ksize=5, stride=1,
            pad=2, initialW=HeNormal(0.1)))
