class_docstr: Implements sampling of image-based features at the 2d projections of a set
  of 3D points, and a subsequent aggregation of the resulting set of features
  per-point.
  
  Args:
      view_sampler: An instance of ViewSampler which is used for sampling of
          image-based features at the 2D projections of a set
          of 3D points.
      feature_aggregator_class_type: The name of the feature aggregator class which
          is available in the global registry.
      feature_aggregator: A feature aggregator class which inherits from
          FeatureAggregatorBase. Typically, the aggregated features and their
          masks are output by a `ViewSampler` which samples feature tensors extracted
          from a set of source images. FeatureAggregator executes step (4) above.
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docstr: Returns the final dimensionality of the output aggregated features.
  
  Args:
      feats: Either a `dict` of sampled features `{f_i: t_i}` corresponding
          to the `feats_sampled` argument of `feature_aggregator,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: Union[Dict[str, torch.Tensor], int]
  ):
      """
      Returns the final dimensionality of the output aggregated features.
  
      Args:
          feats: Either a `dict` of sampled features `{f_i: t_i}` corresponding
              to the `feats_sampled` argument of `feature_aggregator,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.
      """
      return self.feature_aggregator.get_aggregated_feature_dim(feats)
  
file_path: pytorch3d/pytorch3d/implicitron/models/view_pooler/view_pooler.py
imports: from typing import Dict, List, Optional, Union
  import torch
  from pytorch3d.implicitron.tools.config import Configurable, run_auto_creation
  from pytorch3d.renderer.cameras import CamerasBase
  from .feature_aggregator import FeatureAggregatorBase
  from .view_sampler import ViewSampler
label_desc: Use image node_id 2 for calling the ViewPooler obj's underlying member method code with example usage: obj.get_aggregated_feature_dim(feats) and returns: self