[Feature Request] Batched specs of heterogeneous shape and related stacked tensordicts

Motivation

In multiagent settings, each agent's individual spec can differ.
I would be nice to have a way of building heterogeneous composite specs, and carry data using tensordict following this logic.

Solution

1. StackedCompositeSpec
   We could use a StackedCompositeSpec that would essentially work as a tuple of boxes in gym:

Constructor 1

input_spec = StackedCompositeSpec(
    action=[NdUnboundedTensorSpec(-3, 3, shape=[3]), NdUnboundedTensorSpec(-3, 3, shape=[5]), ]
)

Constructor 2

input_spec = StackedCompositeSpec(
    [
     CompositeSpec(action=NdUnboundedTensorSpec(-3, 3, shape=[3])), 
     CompositeSpec(action=NdUnboundedTensorSpec(-3, 3, shape=[5]), 
])

This would basically mean that the environment expects an action of shape Size([3]) for the first agent and Size([5]) for the second.

2. Allowing LazyStackedTensorDict to host tensors of different shape across a dimension

• Construction

tensordict1 = TensorDict({"action": torch.randn(3)}, [])
tensordict2 = TensorDict({"action": torch.randn(5)}, []) # batch size match
out = torch.stack([tensordict1, tensordict2], 0)
print(out)

which would show a tensor

action: Tensor([2, ...], dtype=torch.float32)

where the diverging shapes have been hidden.

• Prohibited operations
  With this kind of tensordict, the get operation would be prohibited. Instead, one could do

out.get_nested_tensor("action")  # LazyStackedTensorDict would create a nestedtensor from it
out[0]["action"]  # returns a regular tensor

Similarly, set_ method would not work (as we don't have a data format to pass the mixed input tensor except nestedtensor).

That way we could carry data in ParallelEnv and using the collector while keeping the key with mixed attributes visible to the users.
One could also access a nestedtensor provided that not more than one LazyTensorDict layer is used (as we can't currently build nested nested tensors).

TensorDictSequential is already capable of handling lazy stacked tensordicts that have differnt keys. We could also think about allowing it (?) to gather tensors that do not share the same shape for instance, although this is harder to implement as not every module has a precise signature of the input tensor it expected.

cc @matteobettini