docstr: Parameters
  ----------
  depth : int, optional
      Specified the layers deep the proposed U-Net should go.
      Layer depth is symmetric on both upsampling and downsampling
      arms.
  max_filter: int, optional
      Specifies the number of filters at the bottom level of the U-Net.
docstr_tok: ["Parameters","--","--","--","--","--","depth",":","int",",","optional","Specified","the","layers","deep","the","proposed","U-Net","should","go",".","Layer","depth","is","symmetric","on","both","upsampling","and","downsampling","arms",".","max_filter",":","int",",","optional","Specifies","the","number","of","filters","at","the","bottom","level","of","the","U-Net","."]
code: def load(self, kwargs):
      """Parameters
      ----------
      depth : int, optional
          Specified the layers deep the proposed U-Net should go.
          Layer depth is symmetric on both upsampling and downsampling
          arms.
      max_filter: int, optional
          Specifies the number of filters at the bottom level of the U-Net.
  
      """
  
      super(PatchDiagram, self).load(kwargs)
  
      add_parameter(self, kwargs, "border_width", 1)
  
file_path: DeepNeuro/deepneuro/outputs/toy.py
imports: import numpy
  from deepneuro.utilities.util import add_parameter
  from deepneuro.outputs.segmentation import PatchesInference
label_desc: Use image node_id 1 for calling the PatchDiagram obj's underlying member method code with example usage: obj.load(kwargs) without return types