code: def weighted_average(
      x: torch.Tensor, weights: Optional[torch.Tensor] = None, dim=None
  ) -> torch.Tensor:
      """
      Computes the weighted average of a given tensor across a given dim, masking
      values associated with weight zero,
      meaning instead of `nan * 0 = nan` you will get `0 * 0 = 0`.
  
      Parameters
      ----------
      x
          Input tensor, of which the average must be computed.
      weights
          Weights tensor, of the same shape as `x`.
      dim
          The dim along which to average `x`
  
      Returns
      -------
      Tensor:
          The tensor with values averaged along the specified `dim`.
      """
      if weights is not None:
          weighted_tensor = torch.where(
              weights != 0, x * weights, torch.zeros_like(x)
          )
          sum_weights = torch.clamp(
              weights.sum(dim=dim) if dim else weights.sum(), min=1.0
          )
          return (
              weighted_tensor.sum(dim=dim)
              if dim
              else weighted_tensor.sum()
          ) / sum_weights
      else:
          return x.mean(dim=dim)
  
imports: import inspect
  from typing import Optional
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling a global function with example usage: weighted_average(x, weights, dim) and returns: unknown, x