class_docstr: Residual linear bottleneck block: x + f(x), f = bottleneck transform.
class_docstr_tok: ["Residual","linear","bottleneck","block",":","x","+","f","(","x",")",",","f","=","bottleneck","transform","."]
code: def forward(self, x):
      return x + self.f(x) if self.has_skip else self.f(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 ResBottleneckLinearBlock obj's underlying member method code with example usage: obj.forward(x) and returns: unknown