class_docstr: A function returning ones for each element. Using this quality function
  for inducing point allocation corresponds to allocating inducing points
  with the sole aim of minimizing predictive variance, i.e. the approach
  of [burt2020svgp]_.
docstr: Args:
      inputs: inputs (of shape n x d)
  
  Returns:
      A tensor of ones for each input, of shape [n]
docstr_tok: ["Args",":","inputs",":","inputs","(","of","shape","n","x","d",")","Returns",":","A","tensor","of","ones","for","each","input",",","of","shape","[","n","]"]
code: def __call__(self, inputs: Tensor) -> Tensor:  # [n, d]-> [n]
      """
      Args:
          inputs: inputs (of shape n x d)
  
      Returns:
          A tensor of ones for each input, of shape [n]
      """
      return torch.ones(
          [inputs.shape[0]], device=inputs.device, dtype=inputs.dtype
      )
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Union
  import torch
  from botorch.exceptions.errors import UnsupportedError
  from botorch.models.model import Model
  from botorch.utils.probability.utils import ndtr, phi
  from gpytorch.module import Module
  from linear_operator.operators import LinearOperator
  from torch import Tensor
label_desc: Use image node_id 1 for calling the UnitQualityFunction obj's underlying member method code with example usage: obj.__call__(inputs) and returns: torch