code: def fit(self, dataset, **kwargs):
      start_step = (
          1 + kwargs["steps_per_epoch"] * kwargs["initial_epoch"]
      )
      self.current_step = tf.Variable(
          start_step, trainable=False, dtype=tf.int32
      )
      self.total_steps = kwargs["epochs"] * kwargs["steps_per_epoch"]
      super().fit(dataset, **kwargs)
  
imports: import tensorflow
  import numpy
  from layers import Mish, ScaledRandomUniform
  import utils
label_desc: Use image node_id 2 for calling the YOLOv4Model obj's underlying member method code with example usage: obj.fit(dataset) without return types