class_docstr: Basic transformation: [3x3 conv, BN, Relu] x2.
class_docstr_tok: ["Basic","transformation",":","[","3x3","conv",",","BN",",","Relu","]","x2","."]
code: def __init__(self, w_in, w_out, stride, _params):
      super(BasicTransform, self).__init__()
      self.a = conv2d(w_in, w_out, 3, stride=stride)
      self.a_bn = norm2d(w_out)
      self.a_af = activation()
      self.b = conv2d(w_out, w_out, 3)
      self.b_bn = norm2d(w_out)
      self.b_bn.final_bn = True
  
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 BasicTransform object from inherited base classes: Module with example: obj = BasicTransform(w_in, w_out, stride, _params)