class_docstr: Aggregates using a set of predefined `reduction_functions` and concatenates
  the results of each aggregation function along the
  channel dimension. The reduction functions singularize the second dimension
  of the sampled features which stacks the source views.
  
  Settings:
      reduction_functions: A list of `ReductionFunction`s` that reduce the
          the stack of source-view-specific features to a single feature.
class_docstr_tok: ["Aggregates","using","a","set","of","predefined","`","reduction_functions","`","and","concatenates","the","results","of","each","aggregation","function","along","the","channel","dimension",".","The","reduction","functions","singularize","the","second","dimension","of","the","sampled","features","which","stacks","the","source","views",".","Settings",":","reduction_functions",":","A","list","of","`","ReductionFunction","`","s","`","that","reduce","the","the","stack","of","source-view-specific","features","to","a","single","feature","."]
code: def get_aggregated_feature_dim(
      self, feats_or_feats_dim: Union[Dict[str, torch.Tensor], int]
  ):
      return _get_reduction_aggregator_feature_dim(
          feats_or_feats_dim, self.reduction_functions
      )
  
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 1 for calling the ReductionFeatureAggregator obj's underlying member method code with example usage: obj.get_aggregated_feature_dim(feats_or_feats_dim) and returns: _get_reduction_aggregator_feature_dim