# tensorplay.functional.linspace

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.linspace.html

```python
tensorplay.functional.linspace(start, end, steps, *, out=None, dtype=None, layout=tensorplay.strided, device=None, requires_grad=False) → Tensor
```

Creates a one-dimensional tensor of size steps whose values are evenly
spaced from start to end, inclusive. That is, the value are:

$$(\text{start},
\text{start} + \frac{\text{end} - \text{start}}{\text{steps} - 1},
\ldots,
\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{\text{steps} - 1},
\text{end})$$

Parameters:

- start ([float](https://docs.python.org/3/builtins/functions.html#float) or [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the starting value for the set of points. If Tensor, it must be 0-dimensional

- end ([float](https://docs.python.org/3/builtins/functions.html#float) or [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the ending value for the set of points. If Tensor, it must be 0-dimensional

- steps ([int](https://docs.python.org/3/builtins/functions.html#int)) – size of the constructed tensor

Keyword Arguments:

- out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

- dtype ([tensorplay.dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType), optional) – the data type to perform the computation in. Default: if None, uses the global default dtype (see tensorplay.get_default_dtype()) when both start and end are real, and corresponding complex dtype when either is complex.

- layout (tensorplay.layout, optional) – the desired layout of returned Tensor. Default: tensorplay.strided.

- device ([tensorplay.device](/docs/generated/tensorplay.Device.html#tensorplay.Device), optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see [tensorplay.set_default_device()](/docs/generated/tensorplay.set_default_device.html#tensorplay.set_default_device)). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

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

Example:

```
>>> tensorplay.linspace(3, 10, steps=5)
tensor([  3.0000,   4.7500,   6.5000,   8.2500,  10.0000])
>>> tensorplay.linspace(-10, 10, steps=5)
tensor([-10.,  -5.,   0.,   5.,  10.])
>>> tensorplay.linspace(start=-10, end=10, steps=5)
tensor([-10.,  -5.,   0.,   5.,  10.])
>>> tensorplay.linspace(start=-10, end=10, steps=1)
tensor([-10.])
```