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 forward(self, x: torch.Tensor) -> torch.Tensor:
      x = self.conv(x)
      if self.norm is not None:
          x = self.norm(x)
      if self.activation is not None:
          x = self.activation(x)
      if self.pool is not None:
          x = self.pool(x)
      return x
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the ResNetBasic3DModule obj's underlying member method code with example usage: obj.forward(x) and returns: x