class_docstr: ResNet basic 3D stem module. Performs spatiotemporal Convolution, BN, and activation
  following by a spatiotemporal pooling.
  
  ::
  
                                      Conv3d
                                         ↓
                                   Normalization
                                         ↓
                                     Activation
                                         ↓
                                      Pool3d
  
  The builder can be found in `create_res_basic_stem`.
docstr: Args:
      conv (torch.nn.modules): convolutional module.
      norm (torch.nn.modules): normalization module.
      activation (torch.nn.modules): activation module.
      pool (torch.nn.modules): pooling module.
docstr_tok: ["Args",":","conv","(","torch.nn.modules",")",":","convolutional","module",".","norm","(","torch.nn.modules",")",":","normalization","module",".","activation","(","torch.nn.modules",")",":","activation","module",".","pool","(","torch.nn.modules",")",":","pooling","module","."]
code: def __init__(
      self,
      *,
      conv: nn.Module = None,
      norm: nn.Module = None,
      activation: nn.Module = None,
      pool: nn.Module = None,
  ) -> None:
      """
      Args:
          conv (torch.nn.modules): convolutional module.
          norm (torch.nn.modules): normalization module.
          activation (torch.nn.modules): activation module.
          pool (torch.nn.modules): pooling module.
      """
      super().__init__()
      set_attributes(self, locals())
      assert self.conv is not None
  
file_path: pytorchvideo/pytorchvideo/models/stem.py
imports: from typing import Callable, Tuple
  import torch
  import torch.nn
  from pytorchvideo.layers.convolutions import ConvReduce3D
  from pytorchvideo.layers.utils import set_attributes
label_desc: Use image node_id 1 to create a new ResNetBasicStem object from inherited base classes: nn with example: obj = ResNetBasicStem()