# tensorplay.functional.hsplit

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

```python
tensorplay.functional.hsplit(input, indices_or_sections) → List of Tensors
```

Splits input, a tensor with one or more dimensions, into multiple tensors
horizontally according to indices_or_sections. Each split is a view of
input.

If input is one dimensional this is equivalent to calling
tensorplay.tensor_split(input, indices_or_sections, dim=0) (the split dimension is
zero), and if input has two or more dimensions it’s equivalent to calling
tensorplay.tensor_split(input, indices_or_sections, dim=1) (the split dimension is 1),
except that if indices_or_sections is an integer it must evenly divide
the split dimension or a runtime error will be thrown.

This function is based on NumPy’s numpy.hsplit().

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – tensor to split.

- indices_or_sections ([int](https://docs.python.org/3/builtins/functions.html#int) or [list](https://docs.python.org/3/builtins/stdtypes.html#list) or [tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple) of ints) – See argument in tensorplay.tensor_split().

Example:

```
>>> t = tensorplay.arange(16.0).reshape(4,4)
>>> t
tensor([[ 0.,  1.,  2.,  3.],
        [ 4.,  5.,  6.,  7.],
        [ 8.,  9., 10., 11.],
        [12., 13., 14., 15.]])
>>> tensorplay.hsplit(t, 2)
(tensor([[ 0.,  1.],
         [ 4.,  5.],
         [ 8.,  9.],
         [12., 13.]]),
 tensor([[ 2.,  3.],
         [ 6.,  7.],
         [10., 11.],
         [14., 15.]]))
>>> tensorplay.hsplit(t, [3, 6])
(tensor([[ 0.,  1.,  2.],
         [ 4.,  5.,  6.],
         [ 8.,  9., 10.],
         [12., 13., 14.]]),
 tensor([[ 3.],
         [ 7.],
         [11.],
         [15.]]),
 tensor([], size=(4, 0)))
```