How can I use tensorflow-privacy in SeqGAN in a right way?

Question

i'm trying to use DPAdamGaussianOptimizer from tensorflow privacy to switch my original optimizer so i could protect privacy during the training. but when i create the DPAdamGaussianOptimizer as i followed the doc:https://tensorflow.google.cn/responsible_ai/privacy/api_docs/python/tf_privacy/v1 and it looks like this:

class Discriminator(object): """ A CNN for text classification. Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer. """

def __init__(
        self, sequence_length, num_classes, vocab_size,
        embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):
    # Placeholders for input, output and dropout
    self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
    self.input_y = tf.placeholder(tf.int64, [None, num_classes], name="input_y")
    # self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
    self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
    # self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")

    # Keeping track of l2 regularization loss (optional)
    l2_loss = tf.constant(0.0)
    
    with tf.variable_scope('discriminator'):

        # Embedding layer
        with tf.device('/cpu:0'), tf.name_scope("embedding"):
            self.W = tf.Variable(
                tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
                name="W")
            self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
            self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)

        # Create a convolution + maxpool layer for each filter size
        pooled_outputs = []
        for filter_size, num_filter in zip(filter_sizes, num_filters):
            with tf.name_scope("conv-maxpool-%s" % filter_size):
                # Convolution Layer
                filter_shape = [filter_size, embedding_size, 1, num_filter]
                W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
                b = tf.Variable(tf.constant(0.1, shape=[num_filter]), name="b")
                conv = tf.nn.conv2d(
                    self.embedded_chars_expanded,
                    W,
                    strides=[1, 1, 1, 1],
                    padding="VALID",
                    name="conv")
                # Apply nonlinearity
                h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
                # Maxpooling over the outputs
                pooled = tf.nn.max_pool(
                    h,
                    ksize=[1, sequence_length - filter_size + 1, 1, 1],
                    strides=[1, 1, 1, 1],
                    padding='VALID',
                    name="pool")
                pooled_outputs.append(pooled)
        
        # Combine all the pooled features
        num_filters_total = sum(num_filters)
        self.h_pool = tf.concat(pooled_outputs, 3)
        self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
        # Add highway
        with tf.name_scope("highway"):
            self.h_highway = highway(self.h_pool_flat, self.h_pool_flat.get_shape()[1], 1, 0)

        # Add dropout
        with tf.name_scope("dropout"):
            self.h_drop = tf.nn.dropout(self.h_highway, self.dropout_keep_prob)

        # Final (unnormalized) scores and predictions
        with tf.name_scope("output"):
            W = tf.Variable(tf.truncated_normal([num_filters_total, num_classes], stddev=0.1), name="W")
            b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
            l2_loss += tf.nn.l2_loss(W)
            l2_loss += tf.nn.l2_loss(b)
            self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")
            self.ypred_for_auc = tf.nn.softmax(self.scores)
            self.predictions = tf.argmax(self.scores, 1, name="predictions")

        # CalculateMean cross-entropy loss
        with tf.name_scope("loss"):
            losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)
            self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss

    self.params = [param for param in tf.trainable_variables() if 'discriminator' in param.name]
    # d_optimizer = tf.train.AdamOptimizer(1e-4)
    d_optimizer=dptf.DPAdamGaussianOptimizer(1.0, 0.001, 1e-4)
    # old_grads_and_vars=old_optimizer.compute_gradients(self.loss, self.params, aggregation_method=2)
    # print(old_grads_and_vars)
    grads_and_vars = d_optimizer.compute_gradients(self.loss, self.params)
    print(grads_and_vars)
    self.train_op = d_optimizer.apply_gradients(grads_and_vars)

it showTypeError: Value passed to parameter 'shape' has DataType float32 not in list of allowed values: int32, int64 my tensorflow version is 1.14 ,python version is 3.6 and my tensorflow privacy is 0.3.0 can anyone help me with it,pls.