code: def init_sound_classifier_graph(self):
      _tf = _lazy_import_tensorflow()
      self.x = _tf.placeholder("float", [None, self.num_inputs])
      self.y = _tf.placeholder("float", [None, self.num_classes])
  
      # Xavier initialization
      initializer = _tf.keras.initializers.glorot_uniform()
  
      weights = {}
      biases = {}
      self.names_of_layers = []
  
      # Create variables for customized layers
      for i, cur_layer_size in enumerate(self.custom_layer_sizes):
          weight_name = "sound_dense{}_weight".format(i)
          bias_name = "sound_dense{}_bias".format(i)
          self.names_of_layers.append("dense{}".format(i))
          out_units = cur_layer_size
          if i == 0:
              in_units = self.num_inputs
          weights[weight_name] = _tf.Variable(
              initializer([in_units, out_units]), name=weight_name
          )
          biases[bias_name] = _tf.Variable(
              initializer([out_units]), name=bias_name
          )
          in_units = out_units
  
      i += 1
      weight_name = "sound_dense{}_weight".format(i)
      bias_name = "sound_dense{}_bias".format(i)
      self.names_of_layers.append("dense{}".format(i))
      weights[weight_name] = _tf.Variable(
          initializer([in_units, self.num_classes]), name=weight_name
      )
      biases[bias_name] = _tf.Variable(
          initializer([self.num_classes]), name=bias_name
      )
  
      (
          self.predictions,
          curr_dense,
      ) = SoundClassifierTensorFlowModel._build_network(
          self.x, weights, biases
      )
  
      # Loss
      self.cost = _tf.reduce_mean(
          _tf.nn.softmax_cross_entropy_with_logits_v2(
              logits=curr_dense, labels=self.y
          )
      )
  
      # Optimizer
      self.optimizer = _tf.train.MomentumOptimizer(
          learning_rate=0.01, momentum=0.9, use_nesterov=True
      ).minimize(self.cost)
  
      # Predictions
      correct_prediction = _tf.equal(
          _tf.argmax(self.predictions, 1), _tf.argmax(self.y, 1)
      )
      self.accuracy = _tf.reduce_mean(
          _tf.cast(correct_prediction, "float")
      )
  
      # Set variables to their initial values
      self.sess.run(_tf.global_variables_initializer())
  
file_path: turicreate/src/python/turicreate/toolkits/sound_classifier/_tf_sound_classifier.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from .._tf_model import TensorFlowModel
  import turicreate.toolkits._tf_utils
  from turicreate._deps.minimal_package import _minimal_package_import_check
label_desc: Use image node_id 5 for calling the SoundClassifierTensorFlowModel obj's underlying member method code with example usage: obj.init_sound_classifier_graph() without return types