# tensorplay.functional.bucketize

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

```python
tensorplay.functional.bucketize(input, boundaries, *, out_int32=False, right=False, out=None) → Tensor
```

Returns the indices of the buckets to which each value in the input belongs, where the
boundaries of the buckets are set by boundaries. Return a new tensor with the same size
as input. If right is False (default), then the left boundary is open. Note that
this behavior is opposite the behavior of
[numpy.digitize](https://numpy.org/doc/stable/reference/generated/numpy.digitize.html).
More formally, the returned index satisfies the following rules:

| right |returned index satisfies |
| --- | --- |
| False | boundaries[i-1] < input[m][n]...[l][x] <= boundaries[i] |
| True | boundaries[i-1] <= input[m][n]...[l][x] < boundaries[i] |

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor) or [Scalar](/docs/generated/tensorplay.Scalar.html#tensorplay.Scalar)) – N-D tensor or a Scalar containing the search value(s).

- boundaries ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – 1-D tensor, must contain a strictly increasing sequence, or the return value is undefined.

Keyword Arguments:

- out_int32 ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – indicate the output data type. tensorplay.int32 if True, tensorplay.int64 otherwise. Default value is False, i.e. default output data type is tensorplay.int64.

- right ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – determines the behavior for values in boundaries. See the table above.

- out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor, must be the same size as input if provided.

Example:

```
>>> boundaries = tensorplay.tensor([1, 3, 5, 7, 9])
>>> boundaries
tensor([1, 3, 5, 7, 9])
>>> v = tensorplay.tensor([[3, 6, 9], [3, 6, 9]])
>>> v
tensor([[3, 6, 9],
        [3, 6, 9]])
>>> tensorplay.bucketize(v, boundaries)
tensor([[1, 3, 4],
        [1, 3, 4]])
>>> tensorplay.bucketize(v, boundaries, right=True)
tensor([[2, 3, 5],
        [2, 3, 5]])
```