code: def build_model(
      activation: str, width: int, depth: int
  ) -> tf.keras.Model:
      """Return the bare model that we'll train."""
      out = inp = tf.keras.layers.Input(shape=(28, 28, 1))
      out = tf.keras.layers.Flatten()(out)
  
      # Stack a bunch of layers of the same width.
      for _ in range(depth):
          out = tf.keras.layers.Dense(width, activation=activation)(out)
  
      # Append a final layer that checks 10 classes.
      out = tf.keras.layers.Dense(10, activation=None)(out)
  
      # return the final model, all sewn together.
      return tf.keras.Model(inputs=inp, outputs=out)
  
imports: from __future__ import division, print_function
  import getpass
  from typing import Dict, Optional
  import fs
  import tensorflow
  import tensorflow_datasets
  import tqdm
  import uv
  import uv.tensorflow.reporter
  import uv.util
  from absl import app
  from uv.fs.reporter import FSReporter
  from uv.mlflow.reporter import MLFlowReporter
  from trainer import cli
label_desc: Use image node_id 3 for calling a global function with example usage: build_model(activation, width, depth) and returns: tf