Extend CheckpointFunction to track all tensor input/output

[000Justin000]

What does this PR do?

The current activation checkpointing implementation would require the input/output argument to be a Tensor to be properly tracked by the auto grad, however for pyspeech nn layers we often use aux_input as a dict and output state which is a list.

This diff enables serialization of a python container: given an input that could be any python "container" (tuple, list, dict), perform a (depth first search) DFS to extract the pytorch tensors from the container and serialize the output to a tuple of tensors. At the original location replace with a index to the serialized list of tensors. As such, the original input can be easily reconstructed.

Before checkpointed_forward, the serialization happens and the tuple of tensors is use as input to forward (thus tracked); during checkpointed_forward, the original input is reconstructed by deserialization and pass in the original forward; the output of the original forward is serialized in the same manner and returned (so that the output is also tracked). After checkpointed_forward, the serialized output is deserialized to the desired format.

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