code: def fit_one_batch(
      self,
      training_data_collection,
      output_model_filepath=None,
      input_groups=None,
      output_directory=None,
      callbacks=["save_model", "log"],
      training_batch_size=16,
      training_steps_per_epoch=None,
      num_epochs=None,
      show_results=False,
      **kwargs,
  ):
      one_batch_generator = self.keras_generator(
          training_data_collection.data_generator(
              perpetual=True,
              data_group_labels=input_groups,
              verbose=False,
              just_one_batch=True,
              batch_size=training_batch_size,
          )
      )
  
      self.callbacks = get_callbacks(
          callbacks,
          output_model_filepath=output_model_filepath,
          data_collection=training_data_collection,
          model=self,
          batch_size=training_batch_size,
          backend="keras",
          **kwargs,
      )
  
      if training_steps_per_epoch is None:
          training_steps_per_epoch = (
              training_data_collection.total_cases
              // training_batch_size
              + 1
          )
  
      try:
          self.model.fit_generator(
              generator=one_batch_generator,
              steps_per_epoch=training_steps_per_epoch,
              epochs=num_epochs,
              callbacks=self.callbacks,
          )
      except KeyboardInterrupt:
          for callback in self.callbacks:
              callback.on_train_end()
      except:
          raise
  
      one_batch = next(one_batch_generator)
      prediction = self.predict(one_batch[0])
  
      if show_results:
          check_data(
              output_data={
                  self.input_data: one_batch[0],
                  self.targets: one_batch[1],
                  "prediction": prediction,
              },
              batch_size=training_batch_size,
          )
  
      return
  
imports: from keras.engine import Input, Model
  from keras.layers import Activation, Lambda
  from keras.layers.merge import concatenate
  from keras.optimizers import Nadam, SGD, Adam, RMSprop, Adagrad, Adamax, Adadelta
  from keras import backend
  from deepneuro.models.cost_functions import dice_coef_loss, dice_coef
  from deepneuro.models.model import DeepNeuroModel
  from deepneuro.utilities.visualize import check_data
  from deepneuro.utilities.util import add_parameter
  from deepneuro.models.callbacks import get_callbacks
  from deepneuro.models.cost_functions import WeightedCategoricalCrossEntropy
label_desc: Use image node_id 3 for calling the KerasModel obj's underlying member method code with example usage: obj.fit_one_batch(training_data_collection, output_model_filepath, input_groups, output_directory, callbacks, training_batch_size, training_steps_per_epoch, num_epochs, show_results) without return types