class_docstr: A simple subnetwork builder that takes feature_columns.
code: def build_subnetwork(
      self,
      features,
      logits_dimension,
      training,
      iteration_step,
      summary,
      previous_ensemble=None,
  ):
      seed = self._seed
      if previous_ensemble:
          # Increment seed so different iterations don't learn the exact same thing.
          seed += 1
  
      with tf_compat.v1.variable_scope("simple"):
          input_layer = tf_compat.v1.feature_column.input_layer(
              features=features,
              feature_columns=tf.feature_column.numeric_column("x", 2),
          )
          last_layer = input_layer
  
      with tf_compat.v1.variable_scope("logits"):
          logits = tf_compat.v1.layers.dense(
              last_layer,
              logits_dimension,
              kernel_initializer=tf_compat.v1.glorot_uniform_initializer(
                  seed=seed
              ),
          )
  
      summary.scalar("scalar", 3)
      batch_size = features["x"].get_shape().as_list()[0]
      summary.image("image", tf.ones([batch_size, 3, 3, 1]))
      with tf_compat.v1.variable_scope("nested"):
          summary.scalar("scalar", 5)
  
      return Subnetwork(
          last_layer=last_layer,
          logits=logits,
          complexity=1,
          persisted_tensors={},
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl import logging
  from adanet import tf_compat
  from adanet.core import testing_utils
  from adanet.core.estimator import Estimator
  from adanet.core.report_materializer import ReportMaterializer
  from adanet.subnetwork import Builder
  from adanet.subnetwork import SimpleGenerator
  from adanet.subnetwork import Subnetwork
  import tensorflow.compat.v2
  from tensorflow_estimator.python.estimator.head import regression_head
label_desc: Use image node_id 3 for calling the _SimpleBuilder obj's underlying member method code with example usage: obj.build_subnetwork(features, logits_dimension, training, iteration_step, summary, previous_ensemble) and returns: Subnetwork