code: def run_optimization(x, y):
      # Wrap computation inside a GradientTape for automatic differentiation.
      with tf.GradientTape() as g:
          # Forward pass.
          logits = neural_net(x, is_training=True)
          # Compute loss.
          loss = cross_entropy_loss(y, logits)
  
      # Variables to update, i.e. trainable variables.
      trainable_variables = neural_net.trainable_variables
  
      # Compute gradients.
      gradients = g.gradient(loss, trainable_variables)
  
      # Update W and b following gradients.
      optimizer.apply_gradients(zip(gradients, trainable_variables))
  
file_path: MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/tf_mnist.py
imports: import numpy
  import argparse
  import os
  import re
  import tensorflow
  import time
  import glob
  from azureml.core import Run
  from utils import load_data
  from tensorflow.keras import Model, layers
label_desc: Use image node_id 3 for calling a global function with example usage: run_optimization(x, y) without return types