class_docstr: MSGNet (from MSG-Net paper)
  Enables passing identity all the way through the generator
  ref https://arxiv.org/abs/1703.06953
  
  Args:
     input_nc(int): Number of input channels, default is 3.
     output_nc(int): Number of output channels, default is 3.
     ngf(int): Number of input channel for middle layer, default is 128.
     n_blocks(int): Block number, default is 6.
     norm_layer(nn.Layer): Batch norm layer, default is nn.InstanceNorm2D.
     load_checkpoint(str): Pretrained checkpoint path, default is None.
  
  Return:
      img(paddle.Tensor): MSGNet output.
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code: def forward(self, input: paddle.Tensor):
      return self.model(input)
  
imports: import os
  import argparse
  import paddle
  import paddle.nn
  import numpy
  import paddle.nn.functional
  from paddlehub.env import MODULE_HOME
  from paddlehub.module.module import moduleinfo
  from paddlehub.vision.transforms import Compose, Resize, CenterCrop
  from paddlehub.module.cv_module import StyleTransferModule
label_desc: Use image node_id 5 for calling the MSGNet obj's underlying member method code with example usage: obj.forward(input) and returns: self