class_docstr: This aggregator does not perform any feature aggregation. Depending on the
  settings the aggregator allows to mask target view features and concatenate
  the outputs.
class_docstr_tok: ["This","aggregator","does","not","perform","any","feature","aggregation",".","Depending","on","the","settings","the","aggregator","allows","to","mask","target","view","features","and","concatenate","the","outputs","."]
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, []
      )
  
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 IdentityFeatureAggregator 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