# tensorplay.tensor

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

```python
tensorplay.tensor(data, *, dtype=None, device=None, requires_grad=False, pin_memory=False) → Tensor
```

Constructs a tensor with no autograd history (also known as a “leaf tensor”, see [Autograd mechanics](/docs/notes/autograd.html)) by copying [data](/docs/generated/tensorplay.data.html#tensorplay.data).

> **Warning**
>
> When working with tensors prefer using tensorplay.Tensor.clone(), tensorplay.Tensor.detach(), and tensorplay.Tensor.requires_grad_() for readability. Letting t be a tensor, tensorplay.tensor(t) is equivalent to t.detach().clone(), and tensorplay.tensor(t, requires_grad=True) is equivalent to t.detach().clone().requires_grad_(True).

> **See also**
>
> tensorplay.as_tensor() preserves autograd history and avoids copies where possible. tensorplay.from_numpy() creates a tensor that shares storage with a NumPy array.

Parameters:

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

Keyword Arguments:

- 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.

- requires_grad ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – If autograd should record operations on the returned tensor. Default: False.

- pin_memory ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

Example:

```
>>> tensorplay.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
tensor([[ 0.1000,  1.2000],
        [ 2.2000,  3.1000],
        [ 4.9000,  5.2000]])

>>> tensorplay.tensor([0, 1])  # Type inference on data
tensor([ 0,  1])

>>> tensorplay.tensor([[0.11111, 0.222222, 0.3333333]],
...              dtype=tensorplay.float64,
...              device=tensorplay.device('cuda:0'))  # creates a double tensor on a CUDA device
tensor([[ 0.1111,  0.2222,  0.3333]], dtype=tensorplay.float64, device='cuda:0')

>>> tensorplay.tensor(3.14159)  # Create a zero-dimensional (scalar) tensor
tensor(3.1416)

>>> tensorplay.tensor([])  # Create an empty tensor (of size (0,))
tensor([])
```