docstr: Parameters
  ----------
  latent_size : int, optional
      Size of the latent vector for image synthesis. Default is 128.
  depth : int, optional
      Specifies the number of layers
  max_filter: int, optional
      Specifies the number of filters at the bottom level of the U-Net.
docstr_tok: ["Parameters","--","--","--","--","--","latent_size",":","int",",","optional","Size","of","the","latent","vector","for","image","synthesis",".","Default","is","128.","depth",":","int",",","optional","Specifies","the","number","of","layers","max_filter",":","int",",","optional","Specifies","the","number","of","filters","at","the","bottom","level","of","the","U-Net","."]
code: def load(self, kwargs):
      """Parameters
      ----------
      latent_size : int, optional
          Size of the latent vector for image synthesis. Default is 128.
      depth : int, optional
          Specifies the number of layers
      max_filter: int, optional
          Specifies the number of filters at the bottom level of the U-Net.
  
      """
  
      super(GAN, self).load(kwargs)
  
      # Generator Parameters
      add_parameter(self, kwargs, "latent_size", 128)
      add_parameter(self, kwargs, "depth", 4)
      add_parameter(self, kwargs, "generator_updates", 1)
  
      # Model Parameters
      add_parameter(self, kwargs, "filter_cap", 128)
      add_parameter(self, kwargs, "filter_floor", 16)
  
      add_parameter(self, kwargs, "generator_max_filter", 128)
  
      # Discriminator Parameters
      add_parameter(self, kwargs, "discriminator_depth", 4)
      add_parameter(self, kwargs, "discriminator_max_filter", 128)
      add_parameter(self, kwargs, "discriminator_updates", 1)
  
      # Loss Parameters
      add_parameter(
          self, kwargs, "gradient_penalty_weight", 10
      )  # For WP
  
      self.sess = None
      self.init = None
  
file_path: DeepNeuro/deepneuro/models/gan.py
imports: import numpy
  import tensorflow
  import os
  from deepneuro.models.tensorflow_model import TensorFlowModel
  from deepneuro.utilities.util import add_parameter
  from deepneuro.models.blocks import generator, discriminator
  from deepneuro.models.cost_functions import wasserstein_loss
label_desc: Use image node_id 1 for calling the GAN obj's underlying member method code with example usage: obj.load(kwargs) without return types