add non_blocking=False args to support transformers>=4.48.0

[AgentDS]

add non_blocking arg in ListWrapper.to to support transformers>=4.48.0.

For transformers<4.48.0, the code of transformers.BatchEncoding.to in src/transformers/tokenization_utils_base.py:

def to(self, device: Union[str, "torch.device"]) -> "BatchEncoding":
    """
    Send all values to device by calling `v.to(device)` (PyTorch only).

    Args:
        device (`str` or `torch.device`): The device to put the tensors on.

    Returns:
        [`BatchEncoding`]: The same instance after modification.
    """
    requires_backends(self, ["torch"])
    import torch

    # This check catches things like APEX blindly calling "to" on all inputs to a module
    # Otherwise it passes the casts down and casts the LongTensor containing the token idxs
    # into a HalfTensor
    if isinstance(device, str) or is_torch_device(device) or isinstance(device, int):
        self.data = {k: v.to(device=device) if isinstance(v, torch.Tensor) else v for k, v in self.data.items()}
    else:
        logger.warning(f"Attempting to cast a BatchEncoding to type {str(device)}. This is not supported.")
    return self

For transformers>=4.48.0, the code of transformers.BatchEncoding.to in src/transformers/tokenization_utils_base.py:

def to(self, device: Union[str, "torch.device"], *, non_blocking: bool = False) -> "BatchEncoding":
    """
    Send all values to device by calling `v.to(device, non_blocking=non_blocking)` (PyTorch only).

    Args:
        device (`str` or `torch.device`): The device to put the tensors on.
        non_blocking (`bool`): Whether to perform the copy asynchronously.

    Returns:
        [`BatchEncoding`]: The same instance after modification.
    """
    requires_backends(self, ["torch"])
    import torch

    # This check catches things like APEX blindly calling "to" on all inputs to a module
    # Otherwise it passes the casts down and casts the LongTensor containing the token idxs
    # into a HalfTensor
    if isinstance(device, str) or is_torch_device(device) or isinstance(device, int):
        self.data = {
            k: v.to(device=device, non_blocking=non_blocking) if isinstance(v, torch.Tensor) else v
            for k, v in self.data.items()
        }
    else:
        logger.warning(f"Attempting to cast a BatchEncoding to type {str(device)}. This is not supported.")
    return self

Thus NEED to add non_blocking for ListWrapper.to function to support transformers>=4.48.0