code: def generate(self, model=None):
      self.generate_output_directory()
  
      if model is not None:
          self.model = model
  
      self.latent_size = self.model.latent_size
  
      for batch_idx in range(0, self.output_num, self.batch_size):
          minibatch_length = min(
              self.batch_size, self.output_num - batch_idx
          )
  
          input_data = {
              "input_data": np.random.normal(
                  self.sampling_mean,
                  self.sampling_std,
                  [minibatch_length, self.latent_size],
              ),
              "casename": [""] * minibatch_length,
              "input_data_augmentation_string": [
                  str(i)
                  for i in range(
                      batch_idx, batch_idx + minibatch_length
                  )
              ],
              "input_data_affine": [None] * minibatch_length,
          }
  
          self.process_case(input_data)
          self.postprocess(input_data)
  
      self.close_output()
  
      return_dict = {
          "data": self.return_objects,
          "filenames": self.return_filenames,
      }
      return return_dict
  
file_path: DeepNeuro/deepneuro/outputs/gan.py
imports: import numpy
  from deepneuro.outputs.inference import ModelInference
  from deepneuro.utilities.util import add_parameter
label_desc: Use image node_id 2 for calling the GANInference obj's underlying member method code with example usage: obj.generate(model) and returns: return_dict