class_docstr: ResNet basic 3D stem module. It performs spatiotemporal Convolution, BN, and activation
  following by a spatiotemporal pooling.
                                      Conv3d
                                         ↓
                                   Normalization
                                         ↓
                                     Activation
                                         ↓
                                      Pool3d
  Args:
      conv (`nn.Module`):
          Convolutional module.
      norm (`nn.Module`):
          Normalization module.
      activation (`nn.Module`):
          Activation module.
      pool (`nn.Module`):
          Pooling module.
class_docstr_tok: ["ResNet","basic","3D","stem","module",".","It","performs","spatiotemporal","Convolution",",","BN",",","and","activation","following","by","a","spatiotemporal","pooling",".","Conv3d","\u2193","Normalization","\u2193","Activation","\u2193","Pool3d","Args",":","conv","(","`","nn.Module","`",")",":","Convolutional","module",".","norm","(","`","nn.Module","`",")",":","Normalization","module",".","activation","(","`","nn.Module","`",")",":","Activation","module",".","pool","(","`","nn.Module","`",")",":","Pooling","module","."]
code: def __init__(
      self,
      *,
      conv=None,
      norm=None,
      activation=None,
      pool=None,
  ) -> None:
      super().__init__()
      self.conv = conv
      self.norm = norm
      self.activation = activation
      self.pool = pool
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new ResNetBasic3DModule object from inherited base classes: nn with example: obj = ResNetBasic3DModule()