我正在探索下面的tensorflow 示例：https://github.com/googledatalab/notebooks/blob/master/samples/TensorFlow/LSTM%20Punctuation%20Model%20With%20TensorFlow.ipynb，它显然是用tf v1编写的，所以我用v2升级脚本升级，有三个主要的不一致：

ERROR: Using member tf.contrib.rnn.DropoutWrapper in deprecated module tf.contrib. tf.contrib.rnn.DropoutWrapper cannot be converted automatically. tf.contrib will not be distributed with TensorFlow 2.0, please consider an alternative in non-contrib TensorFlow, a community-maintained repository such as tensorflow/addons, or fork the required code.
ERROR: Using member tf.contrib.legacy_seq2seq.sequence_loss_by_example in deprecated module tf.contrib. tf.contrib.legacy_seq2seq.sequence_loss_by_example cannot be converted automatically. tf.contrib will not be distributed with TensorFlow 2.0, please consider an alternative in non-contrib TensorFlow, a community-maintained repository such as tensorflow/addons, or fork the required code.
ERROR: Using member tf.contrib.framework.get_or_create_global_step in deprecated module tf.contrib. tf.contrib.framework.get_or_create_global_step cannot be converted automatically. tf.contrib will not be distributed with TensorFlow 2.0, please consider an alternative in non-contrib TensorFlow, a community-maintained repository such as tensorflow/addons, or fork the required code.

因此，为了兼容性，我手动将framework.get_or_create_global_step替换为tf.compat.v1.train.get_or_create_global_step，并将rnn.DropoutWrapper替换为tf.compat.v1.nn.rnn_cell.DropoutWrapper。
但是我无法找到一个解决方案来处理tf.contrib.legacy_seq2seq.sequence_loss_by_example方法，因为我找不到一个向后兼容的替代方法。我尝试安装Tensroflow Addons并使用its seq2seq loss function，但无法找到如何使其适应其余代码。
偶然发现了一些错误，如Consider casting elements to a supported type.或Logits must be a [batch_size x sequence_length x logits] tensor，因为可能我没有正确地实现一些东西。
所以我的问题是：你知道legacy_seq2seq.sequence_loss_by_example是否通过一些我可以使用的第三方插件/库来支持吗？更重要的是，有人能告诉我如何实现这个损失函数的支持tensorflow v2替代方案吗？所以它的行为类似于下面的代码。

output = tf.reshape(tf.concat(axis=1, values=outputs), [-1, size])
    softmax_w = tf.compat.v1.get_variable("softmax_w", [size, len(TARGETS)], dtype=tf.float32)
    softmax_b = tf.compat.v1.get_variable("softmax_b", [len(TARGETS)], dtype=tf.float32)
    logits = tf.matmul(output, softmax_w) + softmax_b
    self._predictions = tf.argmax(input=logits, axis=1)    
    self._targets = tf.reshape(input_.targets, [-1])
    loss = tfa.seq2seq.sequence_loss(
        [logits],
        [tf.reshape(input_.targets, [-1])],
        [tf.ones([batch_size * num_steps], dtype=tf.float32)])
    self._cost = cost = tf.reduce_sum(input_tensor=loss) / batch_size
    self._final_state = state

• 完整代码here *