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


class Parameters(chainer.Chain):

    def __init__(self, chz_channels, channels_x_concat):
        super().__init__(lstm_tanh=L.Convolution2D(None, chz_channels,
            ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)), lstm_i=L.
            Convolution2D(None, chz_channels, ksize=5, stride=1, pad=2,
            initialW=HeNormal(0.1)), lstm_f=L.Convolution2D(None,
            chz_channels, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            lstm_o=L.Convolution2D(None, chz_channels, ksize=5, stride=1,
            pad=2, initialW=HeNormal(0.1)), mean_z=L.Convolution2D(None,
            chz_channels, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            conv_x_concat=L.Convolution2D(None, channels_x_concat, ksize=4,
            stride=4, pad=0, initialW=HeNormal(0.1)), ln_var_z=L.
            Convolution2D(None, chz_channels, ksize=5, stride=1, pad=2,
            initialW=HeNormal(0.1)))

213076a7432132891219bfdf44893d87151e64ac
<mask token>

<mask token>


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

<mask token>


class Parameters(chainer.Chain):

    def __init__(self, chz_channels, channels_x_concat):
        super().__init__(lstm_tanh=L.Convolution2D(None, chz_channels,
            ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)), lstm_i=L.
            Convolution2D(None, chz_channels, ksize=5, stride=1, pad=2,
            initialW=HeNormal(0.1)), lstm_f=L.Convolution2D(None,
            chz_channels, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            lstm_o=L.Convolution2D(None, chz_channels, ksize=5, stride=1,
            pad=2, initialW=HeNormal(0.1)), mean_z=L.Convolution2D(None,
            chz_channels, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            conv_x_concat=L.Convolution2D(None, channels_x_concat, ksize=4,
            stride=4, pad=0, initialW=HeNormal(0.1)), ln_var_z=L.
            Convolution2D(None, chz_channels, 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, chz_channels, channels_x_concat):
        super().__init__(lstm_tanh=L.Convolution2D(None, chz_channels,
            ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)), lstm_i=L.
            Convolution2D(None, chz_channels, ksize=5, stride=1, pad=2,
            initialW=HeNormal(0.1)), lstm_f=L.Convolution2D(None,
            chz_channels, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            lstm_o=L.Convolution2D(None, chz_channels, ksize=5, stride=1,
            pad=2, initialW=HeNormal(0.1)), mean_z=L.Convolution2D(None,
            chz_channels, ksize=5, stride=1, pad=2, initialW=HeNormal(0.1)),
            conv_x_concat=L.Convolution2D(None, channels_x_concat, ksize=4,
            stride=4, pad=0, initialW=HeNormal(0.1)), ln_var_z=L.
            Convolution2D(None, chz_channels, ksize=5, stride=1, pad=2,
            initialW=HeNormal(0.1)))
