code: def forward(
      self, x: LazyTensor, y: LazyTensor, infer_sigma: bool = False
  ) -> LazyTensor:
      dist = ((x - y) ** 2).sum(-1)
  
      if infer_sigma or self.init_required:
          if self.trainable and infer_sigma:
              raise ValueError(
                  "Gradients cannot be computed w.r.t. an inferred sigma value"
              )
          sigma = self.init_sigma_fn(x, y, dist)
          with torch.no_grad():
              self.log_sigma.copy_(sigma.log().clone())
          self.init_required = False
  
      gamma = 1.0 / (2.0 * self.sigma**2)
      gamma = (
          LazyTensor(gamma[None, None, :])
          if len(dist.shape) == 2
          else LazyTensor(gamma[None, None, None, :])
      )
      kernel_mat = (-gamma * dist).exp()
      if len(dist.shape) < len(gamma.shape):
          kernel_mat = kernel_mat.sum(-1) / len(self.sigma)
      return kernel_mat
  
imports: from pykeops.torch import LazyTensor
  import torch
  import torch.nn
  from typing import Callable, Optional, Union
  from alibi_detect.utils.frameworks import Framework
  from alibi_detect.utils._types import Literal
  from copy import deepcopy
label_desc: Use image node_id 3 for calling the GaussianRBF obj's underlying member method code with example usage: obj.forward(x, y, infer_sigma) and returns: kernel_mat