class_docstr: AnyNet stage (sequence of blocks w/ the same output shape).
class_docstr_tok: ["AnyNet","stage","(","sequence","of","blocks","w\/","the","same","output","shape",")","."]
code: def __init__(self, w_in, w_out, stride, d, block_fun, params):
      super(AnyStage, self).__init__()
      for i in range(d):
          block = block_fun(w_in, w_out, stride, params)
          self.add_module("b{}".format(i + 1), block)
          stride, w_in = 1, w_out
  
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 1 to create a new AnyStage object from inherited base classes: Module with example: obj = AnyStage(w_in, w_out, stride, d, block_fun, params)