code: def forward(
      self,
      x: Union[np.ndarray, torch.Tensor],
      y: Union[np.ndarray, torch.Tensor],
      infer_sigma: bool = False,
  ) -> torch.Tensor:
      x, y = torch.as_tensor(x), torch.as_tensor(y)
      dist = distance.squared_pairwise_distance(
          x.flatten(1), y.flatten(1)
      )  # [Nx, Ny]
  
      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)  # [Ns,]
      # TODO: do matrix multiplication after all?
      kernel_mat = torch.exp(
          -torch.cat([(g * dist)[None, :, :] for g in gamma], dim=0)
      )  # [Ns, Nx, Ny]
      return kernel_mat.mean(dim=0)
  
imports: import numpy
  import torch
  from torch import nn
  from .None import distance
  from typing import Optional, Union, Callable
  from alibi_detect.utils.frameworks import Framework
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