# tensorplay.functional.range

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

```python
tensorplay.functional.range(start=0, end, step=1, *, out=None, dtype=None, layout=tensorplay.strided, device=None, requires_grad=False) → Tensor
```

Returns a 1-D tensor of size $\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1$
with values from start to end with step step. Step is
the gap between two values in the tensor.

$$\text{out}_{i+1} = \text{out}_i + \text{step}.$$

> **Warning**
>
> This function is deprecated and will be removed in a future release because its behavior is inconsistent with Python’s range builtin. Instead, use tensorplay.arange(), which produces values in [start, end).

Parameters:

- start ([float](https://docs.python.org/3/builtins/functions.html#float), optional) – the starting value for the set of points. Default: 0.

- end ([float](https://docs.python.org/3/builtins/functions.html#float)) – the ending value for the set of points

- step ([float](https://docs.python.org/3/builtins/functions.html#float), optional) – the gap between each pair of adjacent points. Default: 1.

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 desired data type of returned tensor. Default: if None, uses a global default (see [tensorplay.set_default_dtype()](/docs/generated/tensorplay.set_default_dtype.html#tensorplay.set_default_dtype)). If dtype is not given, infer the data type from the other input arguments. If any of start, end, or step are floating-point, the dtype is inferred to be the default dtype, see [get_default_dtype()](/docs/generated/tensorplay.get_default_dtype.html#tensorplay.get_default_dtype). Otherwise, the dtype is inferred to be tensorplay.int64.

- 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.range(1, 4)
tensor([ 1.,  2.,  3.,  4.])
>>> tensorplay.range(1, 4, 0.5)
tensor([ 1.0000,  1.5000,  2.0000,  2.5000,  3.0000,  3.5000,  4.0000])
```