class_docstr: The MscaleOCRNet implementation based on PaddlePaddle.
  The original article refers to
  Tao et al. "HIERARCHICAL MULTI-SCALE ATTENTION FOR SEMANTIC SEGMENTATION"
  (https://arxiv.org/pdf/2005.10821.pdf).
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (Paddle.nn.Layer): Backbone network.
      backbone_indices (tuple, optional): Two values in the tuple indicate the indices of output of backbone.
          Default: [0].
      mscale (list): The multiple scales for fusion.
          Default: [0.5, 1.0, 2.0].
      pretrained (str, optional): The path or url of pretrained model. 
          Default: None.
class_docstr_tok: ["The","MscaleOCRNet","implementation","based","on","PaddlePaddle",".","The","original","article","refers","to","Tao","et","al",".","``","HIERARCHICAL","MULTI-SCALE","ATTENTION","FOR","SEMANTIC","SEGMENTATION","''","(","https",":","\/\/arxiv.org\/pdf\/2005.10821.pdf",")",".","Args",":","num_classes","(","int",")",":","The","unique","number","of","target","classes",".","backbone","(","Paddle.nn.Layer",")",":","Backbone","network",".","backbone_indices","(","tuple",",","optional",")",":","Two","values","in","the","tuple","indicate","the","indices","of","output","of","backbone",".","Default",":","[","0","]",".","mscale","(","list",")",":","The","multiple","scales","for","fusion",".","Default",":","[","0.5",",","1.0",",","2.0","]",".","pretrained","(","str",",","optional",")",":","The","path","or","url","of","pretrained","model",".","Default",":","None","."]
code: def forward(self, inputs):
      if self.mscale and not self.training:
          return self.nscale_forward(inputs, self.mscale)
      else:
          return self.two_scale_forward(inputs)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.utils import utils
  from paddleseg.cvlibs import manager, param_init
label_desc: Use image node_id 6 for calling the MscaleOCRNet obj's underlying member method code with example usage: obj.forward(inputs) and returns: self, self