class_docstr: Base class for metric computation for evaluating the performance of a model.
  `__call__` is designed to execute the computation.
class_docstr_tok: ["Base","class","for","metric","computation","for","evaluating","the","performance","of","a","model",".","`","__call__","`","is","designed","to","execute","the","computation","."]
docstr: This method should take raw model outputs as inputs, and return values that measure the models' quality.
docstr_tok: ["This","method","should","take","raw","model","outputs","as","inputs",",","and","return","values","that","measure","the","models","'","quality","."]
code: def __call__(self, *args: Any, **kwargs: Any) -> Any:
      """
      This method should take raw model outputs as inputs, and return values that measure the models' quality.
      """
      raise NotImplementedError(
          f"Subclass {self.__class__.__name__} must implement this method."
      )
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from collections.abc import Sequence
  from typing import Any
  import torch
  from monai.config import TensorOrList
  from monai.utils import convert_data_type, evenly_divisible_all_gather
label_desc: Use image node_id 1 for calling the Metric obj's underlying member method code with example usage: obj.__call__() without return types