class_docstr: Bottleneck transformation: Tx1x1, 1x3x3, 1x1x1, where T is the size of
      temporal kernel.
code: def forward(self, x):
      # Explicitly forward every layer.
      # Branch2a.
      x = self.branch2a(x)
      x = self.branch2a_bn(x)
      x = self.branch2a_relu(x)
  
      # Branch2b.
      x = self.branch2b(x)
      x = self.branch2b_bn(x)
      x = self.branch2b_relu(x)
  
      # Branch2c
      x = self.branch2c(x)
      x = self.branch2c_bn(x)
      return x
  
imports: import torch.nn
  from .r2plus1_util import r2plus1_unit
label_desc: Use image node_id 2 for calling the PostactivatedBottleneckTransformation obj's underlying member method code with example usage: obj.forward(x) and returns: x