class_docstr: ResNet basic head. This layer performs an optional pooling operation followed by an
  optional dropout, a fully-connected projection, an optional activation layer and a
  global spatiotemporal averaging.
  
  ::
  
                                      Pool3d
                                         ↓
                                      Dropout
                                         ↓
                                     Projection
                                         ↓
                                     Activation
                                         ↓
                                     Averaging
  
  The builder can be found in `create_res_basic_head`.
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docstr: Args:
      pool (torch.nn.modules): pooling module.
      dropout(torch.nn.modules): dropout module.
      proj (torch.nn.modules): project module.
      activation (torch.nn.modules): activation module.
      output_pool (torch.nn.Module): pooling module for output.
docstr_tok: ["Args",":","pool","(","torch.nn.modules",")",":","pooling","module",".","dropout","(","torch.nn.modules",")",":","dropout","module",".","proj","(","torch.nn.modules",")",":","project","module",".","activation","(","torch.nn.modules",")",":","activation","module",".","output_pool","(","torch.nn.Module",")",":","pooling","module","for","output","."]
code: def __init__(
      self,
      pool: nn.Module = None,
      dropout: nn.Module = None,
      proj: nn.Module = None,
      activation: nn.Module = None,
      output_pool: nn.Module = None,
  ) -> None:
      """
      Args:
          pool (torch.nn.modules): pooling module.
          dropout(torch.nn.modules): dropout module.
          proj (torch.nn.modules): project module.
          activation (torch.nn.modules): activation module.
          output_pool (torch.nn.Module): pooling module for output.
      """
      super().__init__()
      set_attributes(self, locals())
      assert self.proj is not None
  
imports: from typing import Callable, Tuple
  import torch
  import torch.nn
  from pytorchvideo.layers.utils import set_attributes
  from torchvision.ops import RoIAlign
label_desc: Use image node_id 1 to create a new ResNetBasicHead object from inherited base classes: nn with example: obj = ResNetBasicHead(pool, dropout, proj, activation, output_pool)