code: def create_single_fc_model(
      fingerprint_input, model_settings, is_training
  ):
      """Builds a model with a single hidden fully-connected layer.
  
      This is a very simple model with just one matmul and bias layer. As you'd
      expect, it doesn't produce very accurate results, but it is very fast and
      simple, so it's useful for sanity testing.
  
      Here's the layout of the graph:
  
      (fingerprint_input)
              v
          [MatMul]<-(weights)
              v
          [BiasAdd]<-(bias)
              v
  
      Args:
        fingerprint_input: TensorFlow node that will output audio feature vectors.
        model_settings: Dictionary of information about the model.
        is_training: Whether the model is going to be used for training.
  
      Returns:
        TensorFlow node outputting logits results, and optionally a dropout
        placeholder.
      """
      if is_training:
          dropout_rate = tf.compat.v1.placeholder(
              tf.float32, name="dropout_rate"
          )
      fingerprint_size = model_settings["fingerprint_size"]
      label_count = model_settings["label_count"]
      weights = tf.compat.v1.get_variable(
          name="weights",
          initializer=tf.compat.v1.truncated_normal_initializer(
              stddev=0.001
          ),
          shape=[fingerprint_size, label_count],
      )
      bias = tf.compat.v1.get_variable(
          name="bias",
          initializer=tf.compat.v1.zeros_initializer,
          shape=[label_count],
      )
      logits = tf.matmul(fingerprint_input, weights) + bias
      if is_training:
          return logits, dropout_rate
      else:
          return logits
  
file_path: tensorflow-upstream/tensorflow/examples/speech_commands/models.py
imports: import math
  import tensorflow
label_desc: Use image node_id 5 for calling a global function with example usage: create_single_fc_model(fingerprint_input, model_settings, is_training) and returns: logits,  dropout_rate, logits