reference/generated/tensordict.TensorDictBase.from_tuple

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TensorDictBase.from_tuple

classmethod TensorDictBase.from_tuple(obj, *, auto_batch_size: bool = False, batch_dims: int | None = None, device: device | None = None, batch_size: Size | None = None)

Converts a tuple to a TensorDict.

Parameters:

obj – The tuple instance to be converted.

Keyword Arguments:

• auto_batch_size (bool, optional) – If True, the batch size will be computed automatically. Defaults to False.

• batch_dims (int, optional) – If auto_batch_size is True, defines how many dimensions the output tensordict should have. Defaults to None (full batch-size at each level).

• device (device, optional) – The device on which the TensorDict will be created. Defaults to None.

• batch_size (Size, optional) – The batch size of the TensorDict. Defaults to None.

Returns:

A TensorDict representation of the input tuple.

Examples

>>> from tensordict import TensorDict
>>> my_tuple = (1, 2, 3)
>>> td = TensorDict.from_tuple(my_tuple)
>>> print(td)
TensorDict(
    fields={
        0: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
        1: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
        2: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False)

Every tensordict class and every tensorclass has this method. For the other methods, see Methods by task.