class_docstr: Base class for aggregating features.
  
  Typically, the aggregated features and their masks are output by `ViewSampler`
  which samples feature tensors extracted from a set of source images.
  
  Settings:
      exclude_target_view: If `True`/`False`, enables/disables pooling
          from target view to itself.
      exclude_target_view_mask_features: If `True`,
          mask the features from the target view before aggregation
      concatenate_output: If `True`,
          concatenate the aggregated features into a single tensor,
          otherwise return a dictionary mapping feature names to tensors.
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docstr: Returns the final dimensionality of the output aggregated features.
  
  Args:
      feats_or_feats_dim: Either a `dict` of sampled features `{f_i: t_i}` corresponding
          to the `feats_sampled` argument of `forward`,
          or an `int` representing the sum of dimensionalities of each `t_i`.
  
  Returns:
      aggregated_feature_dim: The final dimensionality of the output
          aggregated features.
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code: def get_aggregated_feature_dim(
      self, feats_or_feats_dim: Union[Dict[str, torch.Tensor], int]
  ):
      """
      Returns the final dimensionality of the output aggregated features.
  
      Args:
          feats_or_feats_dim: Either a `dict` of sampled features `{f_i: t_i}` corresponding
              to the `feats_sampled` argument of `forward`,
              or an `int` representing the sum of dimensionalities of each `t_i`.
  
      Returns:
          aggregated_feature_dim: The final dimensionality of the output
              aggregated features.
      """
      raise NotImplementedError()
  
imports: from abc import ABC, abstractmethod
  from enum import Enum
  from typing import Dict, Optional, Sequence, Tuple, Union
  import torch
  import torch.nn.functional
  from pytorch3d.implicitron.models.view_pooler.view_sampler import cameras_points_cartesian_product
  from pytorch3d.implicitron.tools.config import registry, ReplaceableBase
  from pytorch3d.ops import wmean
  from pytorch3d.renderer.cameras import CamerasBase
label_desc: Use image node_id 2 for calling the FeatureAggregatorBase obj's underlying member method code with example usage: obj.get_aggregated_feature_dim(feats_or_feats_dim) without return types