# tensorplay.functional.tensor_split

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

```python
tensorplay.functional.tensor_split(input, indices_or_sections, dim=0) → List of Tensors
```

Splits a tensor into multiple sub-tensors, all of which are views of input,
along dimension dim according to the indices or number of sections specified
by indices_or_sections. This function is based on NumPy’s
numpy.array_split().

Parameters:

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

- indices_or_sections ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), [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) – If indices_or_sections is an integer n or a zero dimensional long tensor with value n, input is split into n sections along dimension dim. If input is divisible by n along dimension dim, each section will be of equal size, input.size(dim) / n. If input is not divisible by n, the sizes of the first int(input.size(dim) % n) sections will have size int(input.size(dim) / n) + 1, and the rest will have size int(input.size(dim) / n). If indices_or_sections is a list or tuple of ints, or a one-dimensional long tensor, then input is split along dimension dim at each of the indices in the list, tuple or tensor. For instance, indices_or_sections=[2, 3] and dim=0 would result in the tensors input[:2], input[2:3], and input[3:]. If indices_or_sections is a tensor, it must be a zero-dimensional or one-dimensional long tensor on the CPU.

- dim ([int](https://docs.python.org/3/builtins/functions.html#int), optional) – dimension along which to split the tensor. Default: 0

Example:

```
>>> x = tensorplay.arange(8)
>>> tensorplay.tensor_split(x, 3)
(tensor([0, 1, 2]), tensor([3, 4, 5]), tensor([6, 7]))

>>> x = tensorplay.arange(7)
>>> tensorplay.tensor_split(x, 3)
(tensor([0, 1, 2]), tensor([3, 4]), tensor([5, 6]))
>>> tensorplay.tensor_split(x, (1, 6))
(tensor([0]), tensor([1, 2, 3, 4, 5]), tensor([6]))

>>> x = tensorplay.arange(14).reshape(2, 7)
>>> x
tensor([[ 0,  1,  2,  3,  4,  5,  6],
        [ 7,  8,  9, 10, 11, 12, 13]])
>>> tensorplay.tensor_split(x, 3, dim=1)
(tensor([[0, 1, 2],
        [7, 8, 9]]),
 tensor([[ 3,  4],
        [10, 11]]),
 tensor([[ 5,  6],
        [12, 13]]))
>>> tensorplay.tensor_split(x, (1, 6), dim=1)
(tensor([[0],
        [7]]),
 tensor([[ 1,  2,  3,  4,  5],
        [ 8,  9, 10, 11, 12]]),
 tensor([[ 6],
        [13]]))
```