这个代码有错误，重新给一个

非常抱歉给你带来了困扰。以下是修正后的可变形卷积的 TensorFlow 2 代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Layer

class DeformableConv2D(Layer):
    def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', **kwargs):
        super(DeformableConv2D, self).__init__(**kwargs)
        self.filters = filters
        self.kernel_size = kernel_size
        self.strides = strides
        self.padding = padding

    def build(self, input_shape):
        num_channels = input_shape[-1]
        kernel_shape = (*self.kernel_size, num_channels, self.filters)

        self.kernel = self.add_weight(name='kernel',
                                      shape=kernel_shape,
                                      initializer='glorot_uniform',
                                      trainable=True)

    def call(self, inputs):
        offsets = tf.random.normal(shape=tf.shape(inputs)[:3] + (2 * self.kernel_size[0] * self.kernel_size[1],))
        outputs = tf.nn.conv2d(inputs, self.kernel, strides=self.strides, padding=self.padding)
        outputs = tf.nn.depthwise_conv2d_native(outputs, offsets, strides=self.strides, padding=self.padding)
        return outputs

    def compute_output_shape(self, input_shape):
        batch_size, height, width, _ = input_shape
        out_height = (height - self.kernel_size[0]) // self.strides[0] + 1
        out_width = (width - self.kernel_size[1]) // self.strides[1] + 1
        return (batch_size, out_height, out_width, self.filters)

# 创建一个模型并使用可变形卷积层
model = tf.keras.Sequential()
model.add(DeformableConv2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding='same', input_shape=(None, None, 3)))
model.add(tf.keras.layers.Activation('relu'))
model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='same'))
model.add(tf.keras.layers.Activation('relu'))
model.add(tf.keras.layers.MaxPool2D(pool_size=(2, 2)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(units=10))
model.add(tf.keras.layers.Activation('softmax'))

# 编译并训练模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

请注意，修复后的代码使用了`tf.nn.depthwise_conv2d_native`函数来替代之前的`tf.nn.convolution`函数，以实现可变形卷积的效果。希望这次能够满足你的需求。如果还有其他问题，请随时提问。