How to use the parl.layers.conv2d function in parl

def __init__(self, act_dim):

        self.conv1 = layers.conv2d(
            num_filters=32, filter_size=8, stride=4, padding=1, act='relu')
        self.conv2 = layers.conv2d(
            num_filters=64, filter_size=4, stride=2, padding=2, act='relu')
        self.conv3 = layers.conv2d(
            num_filters=64, filter_size=3, stride=1, padding=0, act='relu')

        self.fc = layers.fc(size=512, act='relu')

        self.policy_fc = layers.fc(size=act_dim)
        self.value_fc = layers.fc(size=1)

def __init__(self, act_dim):

        self.conv1 = layers.conv2d(
            num_filters=32, filter_size=8, stride=4, padding=1, act='relu')
        self.conv2 = layers.conv2d(
            num_filters=64, filter_size=4, stride=2, padding=2, act='relu')
        self.conv3 = layers.conv2d(
            num_filters=64, filter_size=3, stride=1, padding=0, act='relu')

        self.fc = layers.fc(size=512, act='relu')

        self.policy_fc = layers.fc(size=act_dim)
        self.value_fc = layers.fc(size=1)

def __init__(self, act_dim):

        self.conv1 = layers.conv2d(
            num_filters=16, filter_size=4, stride=2, padding=1, act='relu')
        self.conv2 = layers.conv2d(
            num_filters=32, filter_size=4, stride=2, padding=2, act='relu')
        self.conv3 = layers.conv2d(
            num_filters=256, filter_size=11, stride=1, padding=0, act='relu')

        self.policy_conv = layers.conv2d(
            num_filters=act_dim,
            filter_size=1,
            stride=1,
            padding=0,
            act=None,
            param_attr=ParamAttr(initializer=fluid.initializer.Normal()))

        self.value_fc = layers.fc(
            size=1,
            param_attr=ParamAttr(initializer=fluid.initializer.Normal()))

def __init__(self, act_dim, algo='DQN'):
        self.act_dim = act_dim

        self.conv1 = layers.conv2d(
            num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
        self.conv2 = layers.conv2d(
            num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
        self.conv3 = layers.conv2d(
            num_filters=64, filter_size=4, stride=1, padding=1, act='relu')
        self.conv4 = layers.conv2d(
            num_filters=64, filter_size=3, stride=1, padding=1, act='relu')

        self.algo = algo
        if algo == 'Dueling':
            self.fc1_adv = layers.fc(size=512, act='relu')
            self.fc2_adv = layers.fc(size=act_dim)
            self.fc1_val = layers.fc(size=512, act='relu')
            self.fc2_val = layers.fc(size=1)
        else:
            self.fc1 = layers.fc(size=act_dim)

def __init__(self, act_dim, algo='DQN'):
        self.act_dim = act_dim

        self.conv1 = layers.conv2d(
            num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
        self.conv2 = layers.conv2d(
            num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
        self.conv3 = layers.conv2d(
            num_filters=64, filter_size=4, stride=1, padding=1, act='relu')
        self.conv4 = layers.conv2d(
            num_filters=64, filter_size=3, stride=1, padding=1, act='relu')

        self.algo = algo
        if algo == 'Dueling':
            self.fc1_adv = layers.fc(size=512, act='relu')
            self.fc2_adv = layers.fc(size=act_dim)
            self.fc1_val = layers.fc(size=512, act='relu')
            self.fc2_val = layers.fc(size=1)
        else:
            self.fc1 = layers.fc(size=act_dim)

def __init__(self, act_dim, algo='DQN'):
        self.act_dim = act_dim

        self.conv1 = layers.conv2d(
            num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
        self.conv2 = layers.conv2d(
            num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
        self.conv3 = layers.conv2d(
            num_filters=64, filter_size=4, stride=1, padding=1, act='relu')
        self.conv4 = layers.conv2d(
            num_filters=64, filter_size=3, stride=1, padding=1, act='relu')

        self.algo = algo
        if algo == 'Dueling':
            self.fc1_adv = layers.fc(size=512, act='relu')
            self.fc2_adv = layers.fc(size=act_dim)
            self.fc1_val = layers.fc(size=512, act='relu')
            self.fc2_val = layers.fc(size=1)
        else:
            self.fc1 = layers.fc(size=act_dim)

def __init__(self, multi_conv_layers):
        super(CNN,
              self).__init__([layers.conv2d(**c) for c in multi_conv_layers])