class_docstr: A Coarse-to-Fine Segmentation Network for Small Objects in Remote Sensing Images.
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (paddle.nn.Layer): A backbone network.
      backbone_indices (tuple, optional): The values in the tuple indicate the indices of output of backbone.
         Default: (-1, ).
      kernel_sizes(tuple, optional): The sliding windows' size. Default: (128,128).
      training_stride(int, optional): The stride of sliding windows. Default: 32.
      samples_per_gpu(int, optional): The fined process's batch size. Default: 32.
      channels (int, optional): The channels between conv layer and the last layer of FCNHead.
         If None, it will be the number of channels of input features. Default: None.
      align_corners (bool, optional): An argument of `F.interpolate`. It should be set to False when the output size of feature
         is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.  Default: False.
     pretrained (str, optional): The path or url of pretrained model. Default: None.
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code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
file_path: PaddleSeg/contrib/RSSegBenchmark/c2fnet/models/c2fnet.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import paddleseg.utils.logger
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the C2FNet obj's underlying member method code with example usage: obj.init_weight() without return types