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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def loss(logits, labels):
 """Add L2Loss to all the trainable variables. Add summary for "Loss" and "Loss/avg". Args: logits: Logits from inference(). labels: Labels from distorted_inputs or inputs(). 1-D tensor of shape [batch_size] Returns: Loss tensor of type float. """

  # Calculate the average cross entropy loss across the batch.
  labels = tf.cast(labels, tf.int64)
  cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
      labels=labels, logits=logits, name='cross_entropy_per_example')
  cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
  tf.add_to_collection('losses', cross_entropy_mean)

  # The total loss is defined as the cross entropy loss plus all of the weight
  # decay terms (L2 loss).
  return tf.add_n(tf.get_collection('losses'), name='total_loss')