code: def train_step(images, labels):
      with tf.GradientTape() as tape:
          predictions = model(images)
          loss = loss_object(labels, predictions)
      gradients = tape.gradient(loss, model.trainable_variables)
      optimizer.apply_gradients(
          zip(gradients, model.trainable_variables)
      )
  
      train_loss(loss)
      train_accuracy(labels, predictions)
  
file_path: clearml/examples/frameworks/tensorflow/tensorflow_mnist.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import os
  from tempfile import gettempdir
  import tensorflow
  from tensorflow.keras.layers import Dense, Flatten, Conv2D
  from tensorflow.keras import Model
  from clearml import Task
label_desc: Use image node_id 1 for calling a global function with example usage: train_step(images, labels) without return types