code: def run_keras_custom_training_loop():
      keras_model = keras.Sequential(
          [
              layers.Input(shape=input_shape),
              layers.Dense(64, activation="relu"),
              layers.Dense(8, activation="relu"),
              layers.Dense(num_classes),
              layers.Softmax(),
          ]
      )
      optimizer = optim.Adam(keras_model.parameters(), lr=0.001)
      loss_fn = nn.CrossEntropyLoss()
      train_loop(
          keras_model,
          train_loader,
          num_epochs=num_epochs,
          optimizer=optimizer,
          loss_fn=loss_fn,
          framework="keras",
      )
  
file_path: keras/benchmarks/torch_ctl_benchmark/dense_model_benchmark.py
imports: import numpy
  import torch
  import torch.nn
  import torch.optim
  import keras
  from benchmarks.torch_ctl_benchmark.benchmark_utils import train_loop
  from keras import layers
label_desc: Use image node_id 1 for calling a global function with example usage: run_keras_custom_training_loop() without return types