class_docstr: Bottleneck transformation: 1x1, 3x3 [+SE], 1x1.
class_docstr_tok: ["Bottleneck","transformation",":","1x1",",","3x3","[","+SE","]",",","1x1","."]
code: def forward(self, x):
      for layer in self.children():
          x = layer(x)
      return x
  
imports: from pycls.core.config import cfg
  from pycls.models.blocks import activation, conv2d, conv2d_cx, gap2d, gap2d_cx, init_weights, linear, linear_cx, norm2d, norm2d_cx, pool2d, pool2d_cx, SE
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the BottleneckTransform obj's underlying member method code with example usage: obj.forward(x) and returns: x