code: def forward(ctx, x1, x2, lengthscale, sq_dist_func):
      if any(ctx.needs_input_grad[:2]):
          raise RuntimeError(
              "RBFCovariance cannot compute gradients with "
              "respect to x1 and x2"
          )
      if lengthscale.size(-1) > 1:
          raise ValueError(
              "RBFCovariance cannot handle multiple lengthscales"
          )
      needs_grad = any(ctx.needs_input_grad)
      x1_ = x1.div(lengthscale)
      x2_ = x2.div(lengthscale)
      unitless_sq_dist = sq_dist_func(x1_, x2_)
      # clone because inplace operations will mess with what's saved for backward
      unitless_sq_dist_ = (
          unitless_sq_dist.clone() if needs_grad else unitless_sq_dist
      )
      covar_mat = unitless_sq_dist_.div_(-2.0).exp_()
      if needs_grad:
          d_output_d_input = unitless_sq_dist.mul_(covar_mat).div_(
              lengthscale
          )
          ctx.save_for_backward(d_output_d_input)
      return covar_mat
  
imports: import torch
label_desc: Use image node_id 1 for calling the RBFCovariance obj's underlying member method code with example usage: obj.forward(ctx, x1, x2, lengthscale, sq_dist_func) and returns: covar_mat