你能用tensorflow写一段transformer的代码吗

作为AI语言模型，我可以为您提供一段Transformer的TensorFlow代码，如下所示：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout, LayerNormalization, MultiHeadAttention, Add

def transformer_block(inputs, num_heads, dff, d_model, dropout_rate):
    # Multi-Head Attention
    attention = MultiHeadAttention(num_heads=num_heads, key_dim=d_model)(inputs, inputs)
    attention = Dropout(dropout_rate)(attention)
    attention = LayerNormalization(epsilon=1e-6)(Add()([inputs, attention]))
    
    # Feed Forward Network
    ffn = Dense(dff, activation='relu')(attention)
    ffn = Dropout(dropout_rate)(ffn)
    ffn = Dense(d_model)(ffn)
    ffn = Dropout(dropout_rate)(ffn)
    ffn = LayerNormalization(epsilon=1e-6)(Add()([attention, ffn]))
    
    return ffn

def transformer(input_vocab_size, target_vocab_size, num_heads, dff, d_model, num_layers, dropout_rate):
    # Input Layers
    inputs = Input(shape=(None,))
    targets = Input(shape=(None,))
    
    # Embedding Layers
    input_embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)(inputs)
    target_embedding = tf.keras.layers.Embedding(target_vocab_size, d_model)(targets)
    
    # Positional Encoding
    input_pe = tf.keras.layers.Embedding(input_vocab_size, d_model)(tf.range(start=0, limit=input_vocab_size, delta=1))
    input_pe = tf.keras.layers.Dropout(dropout_rate)(input_pe)
    input_embedding += input_pe
    
    target_pe = tf.keras.layers.Embedding(target_vocab_size, d_model)(tf.range(start=0, limit=target_vocab_size, delta=1))
    target_pe = tf.keras.layers.Dropout(dropout_rate)(target_pe)
    target_embedding += target_pe
    
    # Encoder Layers
    encoder = tf.keras.layers.Dropout(dropout_rate)(input_embedding)
    for i in range(num_layers):
        encoder = transformer_block(encoder, num_heads, dff, d_model, dropout_rate)
    
    # Decoder Layers
    decoder = tf.keras.layers.Dropout(dropout_rate)(target_embedding)
    for i in range(num_layers):
        decoder = transformer_block(decoder, num_heads, dff, d_model, dropout_rate)
    
    # Output Layer
    output = tf.keras.layers.Dense(target_vocab_size, activation='softmax')(decoder)
    
    # Model
    model = tf.keras.models.Model(inputs=[inputs, targets], outputs=output)
    
    return model

这段代码定义了一个Transformer模型，包括输入层、嵌入层、位置编码、多层Transformer块、输出层等。可以通过调整参数来修改模型的配置，以满足您的需求。