请用python编写在tensorflow框架下的tcn与多头注意力机制融合的代码

以下是伪代码，可以帮助您理解如何在TensorFlow框架下融合TCN和多头注意力机制。

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten
from tensorflow.keras.layers import LayerNormalization, MultiHeadAttention

# 定义输入形状为(时间步长，特征维度)
input_shape = (None, feature_dim)
inputs = Input(shape=input_shape)

# TCN模块
tcn_out = inputs
for i in range(num_tcn_layers):
    tcn_out = Conv1D(filters=tcn_filters, kernel_size=tcn_kernel_size, padding='same')(tcn_out)
    tcn_out = LayerNormalization()(tcn_out)
    tcn_out = Dropout(rate=tcn_dropout_rate)(tcn_out)
    tcn_out = MaxPooling1D(pool_size=2)(tcn_out)

# 多头注意力机制
multi_head_out = MultiHeadAttention(num_heads=num_heads, key_dim=key_dim)(inputs, inputs)
multi_head_out = Flatten()(multi_head_out)

# 将TCN和多头注意力机制的输出连接起来
merged = tf.keras.layers.concatenate([tcn_out, multi_head_out])

# 输出层
outputs = Dense(units=output_dim, activation='softmax')(merged)

# 定义模型
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])