dimensions are what remains from the reduction.
Note:
 By default "dim" is "None" resulting in the "input" tensor being
 flattened before computation.

Parameters:
      * input (Tensor) -- the input tensor.
  * **q** (*float** or **Tensor*) -- a scalar or 1D tensor of
    values in the range [0, 1].

  * **dim** (*int*) -- the dimension to reduce.

  * **keepdim** (*bool*) -- whether the output tensor has "dim"
    retained or not.

Keyword Arguments:
      * interpolation (str) -- interpolation method to use when
        the desired quantile lies between two data points. Can be
        "linear", "lower", "higher", "midpoint" and "nearest". Default
        is "linear".
  * **out** (*Tensor**, **optional*) -- the output tensor.

Example:
  >>> a = torch.randn(2, 3)
  >>> a
  tensor([[ 0.0795, -1.2117,  0.9765],
          [ 1.1707,  0.6706,  0.4884]])
  >>> q = torch.tensor([0.25, 0.5, 0.75])
