class_docstr: Bottleneck transformation: 1x1, 3x3, 1x1, only for very deep networks
class_docstr_tok: ["Bottleneck","transformation",":","1x1",",","3x3",",","1x1",",","only","for","very","deep","networks"]
code: def __init__(
      self, w_in, w_out, stride, has_bn, use_srelu, w_b, num_gs
  ):
      super(BottleneckTransform, self).__init__()
      self.has_bn = has_bn
      self.use_srelu = use_srelu
      self._construct(w_in, w_out, stride, w_b, num_gs)
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new BottleneckTransform object from inherited base classes: nn with example: obj = BottleneckTransform(w_in, w_out, stride, has_bn, use_srelu, w_b, num_gs)