code: def get_filter_num(self, depth):
      # This will need to be a bit more complicated; see PGGAN paper.
      if self.max_filter / (2 ** (depth)) <= self.filter_floor:
          return self.filter_floor
      else:
          return min(self.max_filter / (2 ** (depth)), self.filter_cap)
  
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 2 for calling the GAN obj's underlying member method code with example usage: obj.get_filter_num(depth) and returns: self, min