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.
  num_blocks : int, optional
      Number of consecutive convolutional layers at each resolution
      in the U-Net. Default is 2.
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",".","num_blocks",":","int",",","optional","Number","of","consecutive","convolutional","layers","at","each","resolution","in","the","U-Net",".","Default","is","2","."]
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.
      num_blocks : int, optional
          Number of consecutive convolutional layers at each resolution
          in the U-Net. Default is 2.
  
      """
  
      super(UNet, self).load(kwargs)
  
      add_parameter(self, kwargs, "num_blocks", 2)
      add_parameter(self, kwargs, "block_type", "basic")
      add_parameter(self, kwargs, "block_filter_growth_ratio", 2)
      add_parameter(self, kwargs, "depth", 4)
      add_parameter(self, kwargs, "output_channels", 1)
      add_parameter(self, kwargs, "pooling_padding", self.padding)
  
file_path: DeepNeuro/deepneuro/models/unet.py
imports: from keras.layers import Dropout, BatchNormalization, Lambda
  from keras.layers.merge import concatenate
  from deepneuro.models.keras_model import KerasModel
  from deepneuro.models.dn_ops import DnConv, DnMaxPooling, DnDeConv, DnUpsampling, DnZeroPadding
  from deepneuro.utilities.util import add_parameter
label_desc: Use image node_id 1 for calling the UNet obj's underlying member method code with example usage: obj.load(kwargs) without return types