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Closes #27954 

This fixes the hard-coding of packed parameter values for the dynamic quantized LSTM by orchestrating the following dance:

1) Each variadic parameter on the module has its own Module. That Module defines the `__getstate__` and __setstate__` method s.t. packed weights are properly re-done on model load.
2) Each of these modules is wrapped into a `torch.nn.ModuleList`, s.t. the parameters appear as attributes in the hierarchy. Then, `gatherParametersAndBuffers` (https://github.com/pytorch/pytorch/blob/9c43b16df9dad3dfb4da1efab68d8c88e6437e8f/torch/csrc/jit/tracer.cpp#L285) can see these parameters and create a `Value*` for them in the traced graph.
3) In forward, we need to convert from ModuleList -> Module -> Parameter to a simple TensorList of the parameters. We just use a loop here. In tracing, we simply record a `ListConstruct` with each of the proper parameter values. In scripting, the `ModuleList` is const, so it can be unrolled into the graph and a subsequent `ListConstruct` does its business.

The `forward` of the traced LSTM before and after this change are as follows:

Before
```
def forward(self,
    input: Tensor,
    argument_2: Tuple[Tensor, Tensor]) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
  hx, hx0, = argument_2
  _0, _1, _2 = torch.quantized_lstm(input, [hx, hx0], [CONSTANTS.c0, CONSTANTS.c1], True, 1, 0., True, False, False, dtype=12, use_dynamic=True)
  return (_0, (_1, _2))
```

After

```
def forward(self,
    input: Tensor,
    argument_2: Tuple[Tensor, Tensor]) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
  _0 = self.cell._all_weight_values
  _1 = getattr(_0, "0").param
  _2 = getattr(_0, "1").param
  hx, hx0, = argument_2
  _3, _4, _5 = torch.quantized_lstm(input, [hx, hx0], [_1, _2], True, 1, 0., True, False, False, dtype=12, use_dynamic=True)
  return (_3, (_4, _5))

```

Differential Revision: [D18359880](https://our.internmc.facebook.com/intern/diff/D18359880)

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