code: def train_step(self, data):
      input, gt_boxes = data
      with tf.GradientTape() as tape:
          output = self(input, training=True)
          loss0 = calc_loss(0, gt_boxes, output[0])
          loss1 = calc_loss(1, gt_boxes, output[1])
          loss2 = calc_loss(2, gt_boxes, output[2])
          total_loss = loss0 + loss1 + loss2
          gradients = tape.gradient(
              total_loss, self.trainable_variables
          )
          self.optimizer.apply_gradients(
              zip(gradients, self.trainable_variables)
          )
  
      self.loss_tracker.update_state(total_loss)
      self.lr_tracker.update_state(self.optimizer.lr(self.current_step))
      self.current_step.assign_add(1)
  
      return {
          "loss": self.loss_tracker.result(),
          "lr": self.lr_tracker.result(),
      }
  
imports: import tensorflow
  import numpy
  from layers import Mish, ScaledRandomUniform
  import utils
label_desc: Use image node_id 3 for calling the YOLOv4Model obj's underlying member method code with example usage: obj.train_step(data) and returns: dict