class_docstr: GPyTorch Exponential Decay Kernel.
  
  Computes a covariance matrix based on the exponential decay kernel
  between inputs `x_1` and `x_2` (we expect `d = 1`):
  
      K(x_1, x_2) = w + beta^alpha / (x_1 + x_2 + beta)^alpha.
  
  where `w` is an offset parameter, `beta` is a lenthscale parameter, and
  `alpha` is a power parameter.
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code: def _set_power(self, value: Tensor) -> None:
      if not torch.is_tensor(value):
          value = torch.as_tensor(value).to(self.raw_power)
      self.initialize(
          raw_power=self.raw_power_constraint.inverse_transform(value)
      )
  
file_path: botorch/botorch/models/kernels/exponential_decay.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 4 for calling the ExponentialDecayKernel obj's underlying member method code with example usage: obj._set_power(value) without return types