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Translation

beignet.translation_identity 

translation_identity(size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False)

Identity translation vectors.

Parameters:

Name Type Description Default
size int

Output size.

 required
out Tensor

Output tensor. Default, None.

 None
dtype dtype

Type of the returned tensor. Default, global default.

 None
layout layout

Layout of the returned tensor. Default, torch.strided.

 strided
device device

Device of the returned tensor. Default, current device for the default tensor type.

 None
requires_grad bool

Whether autograd records operations on the returned tensor. Default, False.

 False

Returns:

Name Type Description
output Tensor, shape=(size, 3)

Identity rotation quaternions.
Source code in src/beignet/_translation_identity.py 
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def translation_identity(
    size: int,
    *,
    out: Tensor | None = None,
    dtype: torch.dtype | None = None,
    layout: torch.layout | None = torch.strided,
    device: torch.device | None = None,
    requires_grad: bool | None = False,
) -> Tensor:
    """
    Identity translation vectors.

    Parameters
    ----------
    size : int
        Output size.

    out : Tensor, optional
        Output tensor. Default, `None`.

    dtype : torch.dtype, optional
        Type of the returned tensor. Default, global default.

    layout : torch.layout, optional
        Layout of the returned tensor. Default, `torch.strided`.

    device : torch.device, optional
        Device of the returned tensor. Default, current device for the default
        tensor type.

    requires_grad : bool, optional
        Whether autograd records operations on the returned tensor. Default,
        `False`.

    Returns
    -------
    output : Tensor, shape=(size, 3)
        Identity rotation quaternions.
    """
    return torch.zeros(
        [size, 3],
        out=out,
        dtype=dtype,
        layout=layout,
        device=device,
        requires_grad=requires_grad,
    )