# tensorplay.as_tensor

Source: https://www.tensorplay.cn/docs/generated/tensorplay.as_tensor.html

```python
tensorplay.as_tensor(data: Any, dtype: Optional[dtype] = None, device: Optional[DeviceLikeType]) → Tensor
```

Converts [data](/docs/generated/tensorplay.data.html#tensorplay.data) into a tensor, sharing data and preserving autograd
history if possible.

If [data](/docs/generated/tensorplay.data.html#tensorplay.data) is already a tensor with the requested dtype and device
then [data](/docs/generated/tensorplay.data.html#tensorplay.data) itself is returned, but if [data](/docs/generated/tensorplay.data.html#tensorplay.data) is a
tensor with a different dtype or device then it’s copied as if using
data.to(dtype=dtype, device=device).

If [data](/docs/generated/tensorplay.data.html#tensorplay.data) is a NumPy array (an ndarray) with the same dtype and device then a
tensor is constructed using [tensorplay.from_numpy()](/docs/generated/tensorplay.from_numpy.html#tensorplay.from_numpy).

If [data](/docs/generated/tensorplay.data.html#tensorplay.data) is a CuPy array, the returned tensor will be located on the same device as the CuPy array unless
specifically overwritten by [device](/docs/generated/tensorplay.Device.html#tensorplay.Device) or a default device. The device of the CuPy array is inferred from the
pointer of the array using cudaPointerGetAttributes unless [device](/docs/generated/tensorplay.Device.html#tensorplay.Device) is provided with an explicit device index.

> **See also**
>
> tensorplay.tensor() never shares its data and creates a new “leaf tensor” (see Autograd mechanics).

Parameters:

- data (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types.

- dtype ([tensorplay.dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType), optional) – the desired data type of returned tensor. Default: if None, infers data type from [data](/docs/generated/tensorplay.data.html#tensorplay.data).

- device ([tensorplay.device](/docs/generated/tensorplay.Device.html#tensorplay.Device), optional) – the device of the constructed tensor. If None and data is a tensor then the device of data is used. If None and data is not a tensor then the result tensor is constructed on the current device.

Example:

```
>>> a = numpy.array([1, 2, 3])
>>> t = tensorplay.as_tensor(a)
>>> t
tensor([ 1,  2,  3])
>>> t[0] = -1
>>> a
array([-1,  2,  3])

>>> a = numpy.array([1, 2, 3])
>>> t = tensorplay.as_tensor(a, device=tensorplay.device('cuda'))
>>> t
tensor([ 1,  2,  3])
>>> t[0] = -1
>>> a
array([1,  2,  3])
```