code: def calc_mse(
      x: torch.Tensor,
      y: torch.Tensor,
      mask: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      """
      Calculates the mean square error between tensors `x` and `y`.
      """
      if mask is None:
          # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
          return torch.mean((x - y) ** 2)
      else:
          # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
          return (((x - y) ** 2) * mask).sum() / mask.expand_as(
              x
          ).sum().clamp(1e-5)
  
file_path: pytorch3d/pytorch3d/implicitron/tools/metric_utils.py
imports: import math
  from typing import Optional, Tuple
  import torch
  from torch.nn import functional
label_desc: Use image node_id 4 for calling a global function with example usage: calc_mse(x, y, mask) and returns: torch, unknown