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# torch.kthvalue

torch.kthvalue(input, k, dim=None, keepdim=False, *, out=None)

  Returns a namedtuple `(values, indices)` where `values` is the `k` th smallest element of each row of the `input` tensor in the given dimension `dim`. And `indices` is the index location of each element found.

  If `dim` is not given, the last dimension of the input is chosen.

  If `keepdim` is `True`, both the `values` and `indices` tensors are the same size as `input`, except in the dimension `dim` where they are of size 1. Otherwise, `dim` is squeezed (see `torch.squeeze()`), resulting in both the `values` and `indices` tensors having 1 fewer dimension than the `input` tensor.

  Note

  When `input` is a CUDA tensor and there are multiple valid `k` th values, this function may nondeterministically return `indices` for any of them.

  Parameters:

    - input (Tensor) – the input tensor.

    - k (int) – k for the k-th smallest element

    - dim (int, optional) – the dimension to find the kth value along

    - keepdim (bool) – whether the output tensor has `dim` retained or not.

  Keyword Arguments:

    out (tuple, optional) – the output tuple of (Tensor, LongTensor) can be optionally given to be used as output buffers

  Example:

      >>> x = torch.arange(1., 6.)
      >>> x
      tensor([ 1.,  2.,  3.,  4.,  5.])
      >>> torch.kthvalue(x, 4)
      torch.return_types.kthvalue(values=tensor(4.), indices=tensor(3))

      >>> x=torch.arange(1.,7.).resize_(2,3)
      >>> x
      tensor([[ 1.,  2.,  3.],
              [ 4.,  5.,  6.]])
      >>> torch.kthvalue(x, 2, 0, True)
      torch.return_types.kthvalue(values=tensor([[4., 5., 6.]]), indices=tensor([[1, 1, 1]]))

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