class_docstr: Bottleneck transformation with pre-activation, which includes BatchNorm3D
      and ReLu. Conv3D kernsl are Tx1x1, 1x3x3, 1x1x1, where T is the size of
      temporal kernel (https://arxiv.org/abs/1603.05027).
code: def forward(self, x):
      # Branch2a
      if not self.disable_pre_activation:
          x = self.branch2a_bn(x)
          x = self.branch2a_relu(x)
      x = self.branch2a(x)
      # Branch2b
      x = self.branch2b_bn(x)
      x = self.branch2b_relu(x)
      x = self.branch2b(x)
      # Branch2c
      x = self.branch2c_bn(x)
      x = self.branch2c_relu(x)
      x = self.branch2c(x)
      return x
  
imports: import torch.nn
  from .r2plus1_util import r2plus1_unit
label_desc: Use image node_id 2 for calling the PreactivatedBottleneckTransformation obj's underlying member method code with example usage: obj.forward(x) and returns: x