reference/generated/tensordict.from_tuple

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from_tuple

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

Converts a tuple to a tensordict with the keys "0", "1", …

Each element is converted with from_any(). See TensorDictBase.from_tuple.

Parameters:

obj (tuple) – the tuple to convert.

Keyword Arguments:

• auto_batch_size (bool, optional) – if True, sets the batch size to the largest leading shape that all the entries share. Exclusive with batch_size. Defaults to False.

• batch_dims (int, optional) – with auto_batch_size, the maximum number of batch dimensions. Ignored without auto_batch_size, with a FutureWarning when it would change the result. Starting with TensorDict 0.17, the default will change to turning on auto_batch_size when batch_dims is given, as from_dict does. Defaults to None.

• device (device, str or int, optional) – the device of the result. Leaves on another device are moved to it. None leaves each leaf where it is, and the result has no device. Defaults to None.

• batch_size (Size or list of int, optional) – the batch size of the result: the leading dimensions that all its entries share. Exclusive with auto_batch_size. Defaults to None, an empty batch size.

Returns:

A TensorDict.

Examples

>>> import torch
>>> from tensordict import from_tuple
>>> td = from_tuple((torch.zeros(3, 4), torch.ones(3)), auto_batch_size=True)
>>> list(td.keys()), td.batch_size
(['0', '1'], torch.Size([3]))