code: def __init__(self, **kwargs):
      add_parameter(self, kwargs, "data_collection", None)
      add_parameter(
          self,
          kwargs,
          "epoch_prediction_data_collection",
          self.data_collection,
      )
      add_parameter(self, kwargs, "epoch_prediction_object", None)
      add_parameter(self, kwargs, "deepneuro_model", None)
      add_parameter(self, kwargs, "epoch_prediction_dir", None)
      add_parameter(self, kwargs, "output_gif", None)
      add_parameter(self, kwargs, "batch_size", 1)
      add_parameter(
          self, kwargs, "epoch_prediction_batch_size", self.batch_size
      )
      add_parameter(self, kwargs, "latent_size", 128)
      add_parameter(self, kwargs, "sample_latent", None)
  
      if self.sample_latent is None:
          self.sample_latent = np.random.normal(
              size=[self.epoch_prediction_batch_size, self.latent_size]
          )
  
      if not os.path.exists(self.epoch_prediction_dir):
          os.mkdir(self.epoch_prediction_dir)
  
      self.predictions = []
      self.depth_directories = []
  
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 to create a new PGGANPredict object from inherited base classes: keras.callbacks with example: obj = PGGANPredict()