class_docstr: A feature map defined by centering a kernel at a set of points.
code: def num_outputs(self) -> int:
      if self.output_transform is None:
          return self.points.shape[-1]
  
      canary = torch.empty(
          1,
          self.points.shape[-1],
          device=self.points.device,
          dtype=self.points.dtype,
      )
      return self.output_transform(canary).shape[-1]
  
imports: from __future__ import annotations
  from typing import Optional, Union
  import torch
  from botorch.sampling.pathwise.utils import TInputTransform, TOutputTransform, TransformedModuleMixin
  from gpytorch.kernels import Kernel
  from linear_operator.operators import LinearOperator
  from torch import Size, Tensor
  from torch.nn import Module
label_desc: Use image node_id 3 for calling the KernelEvaluationMap obj's underlying member method code with example usage: obj.num_outputs() and returns: self, self