lucid.full_like

lucid.full_like(a: Tensor | list | ndarray | array, fill_value: int | float | complex, dtype: type[bool | int | float | complex] | Numeric | None = None, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] | None = None) → Tensor

The full_like function returns a new Tensor with the same shape as the input, filled with a specified scalar value.

Function Signature

def full_like(
    a: Tensor | _ArrayLike,
    fill_value: _Scalar,
    dtype: _BuiltinNumeric | Numeric | None = None,
    requires_grad: bool = False,
    keep_grad: bool = False,
    device: _DeviceType | None = None,
) -> Tensor

Parameters

• a (Tensor | array-like): Reference tensor (or array-like) whose shape will be used for the new tensor.

• fill_value (_Scalar): The constant value to fill the new tensor with.

• dtype (type | Numeric, optional): Desired data type for the tensor. Can be a built-in numeric type or a Numeric type. If None, uses the dtype of a.

• requires_grad (bool, optional): If set to True, gradients will be tracked for this tensor. Defaults to False.

• keep_grad (bool, optional): Whether to retain the gradient after a backward pass. Defaults to False.

• device (Literal[“cpu”, “gpu”] | None, optional): Target device for the new tensor. If None, uses the device of a.

Returns

• Tensor: A new tensor filled with fill_value, with shape matching a.

Examples

>>> import lucid
>>> a = lucid.Tensor([[1, 2], [3, 4]])
>>> t = lucid.full_like(a, fill_value=9)
>>> print(t)
Tensor([[9, 9],
        [9, 9]], grad=None)

>>> # With GPU and gradient tracking
>>> t = lucid.full_like(a, 0.0, dtype=lucid.Float32, requires_grad=True, device="gpu")
>>> print(t.requires_grad)
True
>>> print(t.device)
gpu

Notes

Tip

This function is useful when creating a tensor that mimics the shape of an existing one, but with uniform values.

Warning

If device is not specified, full_like defaults to the device of the input tensor a.