code: def calculate_l2_and_summaries(predicted_vectors, true_vectors, name):
      """Calculate L2 loss and associated summaries.
  
      Args:
        predicted_vectors: Tensor of predictions, first dimension is batch size.
        true_vectors: Tensor of labels, first dimension is batch size.
        name: Name to use as prefix for summaries.
  
      Returns:
        loss: Dimensionless tensor representing the mean euclidean distance
          between true and predicted.
      """
      loss = tf.reduce_mean((predicted_vectors - true_vectors) ** 2)
      tf.summary.scalar(name + "_loss", loss)
      tf.summary.scalar(
          name + "_prediction_mean_squared_norm",
          tf.reduce_mean(tf.nn.l2_loss(predicted_vectors)),
      )
      tf.summary.scalar(
          name + "_label_mean_squared_norm",
          tf.reduce_mean(tf.nn.l2_loss(true_vectors)),
      )
      return loss
  
imports: import importlib
  import os
  import librosa
  import numpy
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 22 for calling a global function with example usage: calculate_l2_and_summaries(predicted_vectors, true_vectors, name) and returns: loss