[Feature] A pydantic tensorclass implementation

[vmoens]

Test:

from tensordict.tensorclass_v2 import TensorClass, NestedList
import torch
from torch import Tensor


class MyClass(TensorClass):
    a: int | NestedList[int]  # NonTensorStack
    b: str | NestedList[str]  # Now accepts str or list[str] or list[list[str]] etc
    c: Tensor
    d: str  # MetaData


# Default (empty) batch size
model = MyClass(a=1, b="hello", c=torch.tensor([1.0, 2.0, 3.0]), d="a string")
print(f"{model=}")
print(f"Model attributes: a={model.a}, b={model.b}")
print(f"Model tensor c={model.c}")
print(f"TensorDict contents: {model._tensordict}")
print(f"Default batch_size: {model.batch_size}")

# Integer batch size
model = MyClass(
    a=[1, 2],
    b=["hello", "world"],
    c=torch.tensor([[1.0], [2.0]]),  # 2x1 tensor
    d="a string",
    batch_size=2,
)
print(f"{model=}")
print(f"{model._tensordict=}")
print(f"Integer batch_size: {model.batch_size}")

print(f"{model.to_tensordict()=}")
print(f"{model.cpu()=}")
print(f"{model.to('mps')=}")
print(f"{model.unbind(0)=}")
print(f"{model.split(1)=}")
print(f"{model.chunk(2)=}")