code: def calculate_softmax_and_summaries(logits, one_hot_labels, name):
      """Calculate the softmax cross entropy loss and associated summaries.
  
      Args:
        logits: Tensor of logits, first dimension is batch size.
        one_hot_labels: Tensor of one hot encoded categorical labels. First
          dimension is batch size.
        name: Name to use as prefix for summaries.
  
      Returns:
        loss: Dimensionless tensor representing the mean negative
          log-probability of the true class.
      """
      loss = tf.nn.softmax_cross_entropy_with_logits(
          logits=logits, labels=one_hot_labels
      )
      loss = tf.reduce_mean(loss)
      softmax_summaries(loss, logits, one_hot_labels, name)
      return loss
  
imports: import importlib
  import os
  import librosa
  import numpy
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 19 for calling a global function with example usage: calculate_softmax_and_summaries(logits, one_hot_labels, name) and returns: loss