class_docstr: A kernel that supports spectral learning for GPs, where the underlying spectral density is modeled as a mixture
  of delta distributions (e.g., with point masses). This has been explored e.g. in Lazaro-Gredilla et al., 2010.
  
  Conceptually, this kernel is similar to random Fourier features as implemented in RFFKernel, but instead of sampling
  a Gaussian to determine the spectrum sites, they are treated as learnable parameters.
  
  When using CG for inference, this kernel supports linear space and time (in N) for training and inference.
  
  :param int num_dims: Dimensionality of input data that this kernel will operate on. Note that if active_dims is
      used, this should be the length of the active dim set.
  :param int num_deltas: Number of point masses to learn.
code: def Z(self, value):
      self._set_Z(value)
  
file_path: gpytorch/gpytorch/kernels/spectral_delta_kernel.py
imports: import math
  from typing import Optional
  import torch
  from linear_operator.operators import MatmulLinearOperator, RootLinearOperator
  from ..constraints import Interval, Positive
  from .kernel import Kernel
label_desc: Use image node_id 5 for calling the SpectralDeltaKernel obj's underlying member method code with example usage: obj.Z(value) without return types