code: def estimator_fn(run_config, hyperparameters):
      input_tensor_name = hyperparameters.get(
          "input_tensor_name", "inputs"
      )
      learning_rate = hyperparameters.get("learning_rate", 0.05)
      feature_columns = [
          tf.feature_column.numeric_column(input_tensor_name, shape=[4])
      ]
      return tf.estimator.DNNClassifier(
          feature_columns=feature_columns,
          hidden_units=[10, 20, 10],
          optimizer=tf.train.AdagradOptimizer(
              learning_rate=learning_rate
          ),
          n_classes=3,
          config=run_config,
      )
  
file_path: sagemaker-python-sdk/tests/data/iris/iris-dnn-classifier.py
imports: from __future__ import absolute_import
  import numpy
  import os
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: estimator_fn(run_config, hyperparameters) and returns: tf