class_docstr: GPyTorch Downsampling Kernel.
  
  Computes a covariance matrix based on the down sampling kernel between
  inputs `x_1` and `x_2` (we expect `d = 1`):
  
      K(\mathbf{x_1}, \mathbf{x_2}) = c + (1 - x_1)^(1 + delta) *
          (1 - x_2)^(1 + delta).
  
  where `c` is an offset parameter, and `delta` is a power parameter.
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code: def power(self, value: Tensor) -> None:
      self._set_power(value)
  
file_path: botorch/botorch/models/kernels/downsampling.py
imports: from __future__ import annotations
  from typing import Optional
  import torch
  from gpytorch.constraints import Interval, Positive
  from gpytorch.kernels import Kernel
  from gpytorch.priors import Prior
  from torch import Tensor
label_desc: Use image node_id 3 for calling the DownsamplingKernel obj's underlying member method code with example usage: obj.power(value) without return types