code: def get_callbacks(
      callbacks=["save_model", "early_stopping", "log"],
      output_model_filepath=None,
      monitor="val_loss",
      model=None,
      data_collection=None,
      save_best_only=False,
      epoch_prediction_dir=None,
      batch_size=1,
      epoch_prediction_object=None,
      epoch_prediction_data_collection=None,
      epoch_prediction_batch_size=None,
      latent_size=128,
      backend="tensorflow",
      cyclic_base_learning_rate=0.001,
      cyclic_max_learning_rate=0.006,
      learning_rate_cycle=2000,
      **kwargs,
  ):
      """Very disorganized currently. Replace with dictionary? Also address never-ending parameters"""
  
      return_callbacks = []
  
      for callback in callbacks:
          if callback == "save_model":
              if backend == "keras":
                  return_callbacks += [
                      ModelCheckpoint(
                          output_model_filepath,
                          monitor=monitor,
                          save_best_only=save_best_only,
                      )
                  ]
              else:
                  return_callbacks += [SaveModel(deepneuro_model=model)]
  
          if callback == "early_stopping":
              return_callbacks += [
                  EarlyStopping(monitor=monitor, patience=10)
              ]
  
          if callback == "log":
              return_callbacks += [
                  CSVLogger(
                      output_model_filepath.replace(".h5", ".log")
                  )
              ]
  
          if callback == "cyclic_learning_rate":
              return_callbacks += [
                  CyclicLR(
                      base_lr=cyclic_base_learning_rate,
                      max_lr=cyclic_max_learning_rate,
                      step_size=learning_rate_cycle,
                  )
              ]
  
          if callback == "predict_epoch":
              return_callbacks += [
                  EpochPredict(
                      deepneuro_model=model,
                      prediction_data_collection=data_collection,
                      epoch_prediction_dir=epoch_prediction_dir,
                      epoch_prediction_object=epoch_prediction_object,
                      epoch_prediction_data_collection=epoch_prediction_data_collection,
                      epoch_prediction_batch_size=epoch_prediction_batch_size,
                      **kwargs,
                  )
              ]
  
          if callback == "predict_gan":
              return_callbacks += [
                  GANPredict(
                      deepneuro_model=model,
                      data_collection=data_collection,
                      epoch_prediction_dir=epoch_prediction_dir,
                      batch_size=batch_size,
                      epoch_prediction_object=epoch_prediction_object,
                      epoch_prediction_data_collection=epoch_prediction_data_collection,
                      epoch_prediction_batch_size=epoch_prediction_batch_size,
                      latent_size=latent_size,
                  )
              ]
  
      return return_callbacks
  
imports: import os
  import imageio
  import numpy
  from keras.callbacks import ModelCheckpoint, EarlyStopping, CSVLogger, Callback
  from keras import backend
  from deepneuro.utilities.util import add_parameter
  from deepneuro.utilities.visualize import check_data
label_desc: Use image node_id 1 for calling a global function with example usage: get_callbacks(callbacks, output_model_filepath, monitor, model, data_collection, save_best_only, epoch_prediction_dir, batch_size, epoch_prediction_object, epoch_prediction_data_collection, epoch_prediction_batch_size, latent_size, backend, cyclic_base_learning_rate, cyclic_max_learning_rate, learning_rate_cycle) and returns: return_callbacks