# tensorplay.functional.quantize_per_tensor_dynamic

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

```python
tensorplay.functional.quantize_per_tensor_dynamic(input, dtype, reduce_range) → Tensor
```

Converts a float tensor to a quantized tensor with scale and zero_point calculated
dynamically based on the input.

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – float tensor or list of tensors to quantize

- dtype ([tensorplay.dtype](/docs/generated/tensorplay.DType.html#tensorplay.DType)) – the desired data type of returned tensor. Has to be one of the quantized dtypes: tensorplay.quint8, tensorplay.qint8

- reduce_range ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – a flag to indicate whether to reduce the range of quantized

- bit (data by 1)

- hardwares (it's required to avoid instruction overflow for some)

Returns:

A newly (dynamically) quantized tensor

Return type:

[Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)

Example:

```
>>> t = tensorplay.quantize_per_tensor_dynamic(tensorplay.tensor([-1.0, 0.0, 1.0, 2.0]), tensorplay.quint8, False)
>>> print(t)
tensor([-1.,  0.,  1.,  2.], size=(4,), dtype=tensorplay.quint8,
       quantization_scheme=tensorplay.per_tensor_affine, scale=0.011764705882352941,
       zero_point=85)
>>> t.int_repr()
tensor([  0,  85, 170, 255], dtype=tensorplay.uint8)
```