code: def _find_output_mode_and_verify(
      initial_eval: Union[int, float, Tensor],
      num_examples: int,
      perturbations_per_eval: int,
      feature_mask: Union[None, TensorOrTupleOfTensorsGeneric],
      allow_multi_outputs: bool = False,
  ) -> bool:
      """
      This method identifies whether the model outputs a single output for a batch
      (agg_output_mode = True) or whether it outputs a single output per example
      (agg_output_mode = False) and returns agg_output_mode. The method also
      verifies that perturbations_per_eval is 1 in the case that agg_output_mode is True
      and also verifies that the first dimension of each feature mask if the model
      returns a single output for a batch.
      """
      if isinstance(initial_eval, (int, float)) or (
          isinstance(initial_eval, torch.Tensor)
          and (
              len(initial_eval.shape) == 0
              or (num_examples > 1 and initial_eval.numel() == 1)
          )
      ):
          agg_output_mode = True
          assert (
              perturbations_per_eval == 1
          ), "Cannot have perturbations_per_eval > 1 when function returns scalar."
          if feature_mask is not None:
              for single_mask in feature_mask:
                  assert single_mask.shape[0] == 1, (
                      "Cannot provide different masks for each example when function "
                      "returns a scalar."
                  )
      else:
          agg_output_mode = False
          if not allow_multi_outputs:
              assert (
                  isinstance(initial_eval, torch.Tensor)
                  and initial_eval[0].numel() == 1
              ), "Target should identify a single element in the model output."
      return agg_output_mode
  
imports: import typing
  from inspect import signature
  from typing import Any, Callable, List, Tuple, TYPE_CHECKING, Union
  import torch
  from captum._utils.common import _format_baseline, _format_output, _format_tensor_into_tuples, _validate_input
  from captum._utils.typing import BaselineType, Literal, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.approximation_methods import SUPPORTED_METHODS
  from torch import Tensor
label_desc: Use image node_id 19 for calling a global function with example usage: _find_output_mode_and_verify(initial_eval, num_examples, perturbations_per_eval, feature_mask, allow_multi_outputs) and returns: agg_output_mode