docstr: Parameters
  ----------
  initial_size : tuple
docstr_tok: ["Parameters","--","--","--","--","--","initial_size",":","tuple"]
code: def load(self, kwargs):
      """Parameters
      ----------
      initial_size : tuple
  
  
      """
  
      super(PGGAN, self).load(kwargs)
  
      add_parameter(self, kwargs, "initial_size", (4, 4))
  
      # Dummy training variables.
      add_parameter(self, kwargs, "num_epochs", 1)
      add_parameter(self, kwargs, "training_steps_per_epoch", 1)
      add_parameter(self, kwargs, "training_batch_size", 16)
      add_parameter(self, kwargs, "batch_size", None)
  
      if self.batch_size is not None:
          self.training_batch_size = self.batch_size
  
      if type(self.initial_size) not in [list, tuple]:
          self.initial_size = (4,) * self.dim
  
      # PGGAN Parameters
      self.starting_depth = 1
      self.transition_dict = {True: "_Transition", False: ""}
      self.progressive_depth = self.depth
      self.transition = False
  
      if self.dim == 3:
          raise NotImplementedError
  
imports: import math
  import numpy
  import tensorflow
  import os
  import imageio
  import keras
  import scipy
  from tqdm import tqdm
  from collections import defaultdict
  from deepneuro.utilities.util import add_parameter
  from deepneuro.models.blocks import generator, discriminator
  from deepneuro.models.cost_functions import wasserstein_loss
  from deepneuro.models.gan import GAN
  from deepneuro.models.ops import upscale2d, downscale2d
  from deepneuro.utilities.visualize import check_data
label_desc: Use image node_id 1 for calling the PGGAN obj's underlying member method code with example usage: obj.load(kwargs) without return types