class_docstr: Combine Instance Norm and Batch Norm into One Layer
class_docstr_tok: ["Combine","Instance","Norm","and","Batch","Norm","into","One","Layer"]
code: def forward(self, x):
      bn_x = self.bnorm(x[:, : self.bnorm_channels, :, :])
      in_x = self.inorm(x[:, self.bnorm_channels :, :, :])
  
      return paddle.concat((bn_x, in_x), 1)
  
imports: from collections import defaultdict
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  import scipy
  import paddleseg
  from paddleseg.models import layers, losses
  from paddleseg import utils
  from paddleseg.cvlibs import manager, param_init
label_desc: Use image node_id 2 for calling the IBNorm obj's underlying member method code with example usage: obj.forward(x) and returns: paddle