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**, **optional*) -- dimension along which to
    split the tensor. Default: "0"

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

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