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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, batch_size=None):
 """Adds all losses for the model. Note the final loss is not returned. Instead, the list of losses are collected by slim.losses. The losses are accumulated in tower_loss() and summed to calculate the total loss. Args: logits: List of logits from inference(). Each entry is a 2-D float Tensor. labels: Labels from distorted_inputs or inputs(). 1-D tensor of shape [batch_size] batch_size: integer """

  if not batch_size:
    batch_size = FLAGS.batch_size

  # Reshape the labels into a dense Tensor of
  # shape [FLAGS.batch_size, num_classes].
  sparse_labels = tf.reshape(labels, [batch_size, 1])
  indices = tf.reshape(tf.range(batch_size), [batch_size, 1])
  concated = tf.concat(axis=1, values=[indices, sparse_labels])
  num_classes = logits[0].get_shape()[-1].value
  dense_labels = tf.sparse_to_dense(concated,
                                    [batch_size, num_classes],
                                    1.0, 0.0)

  # Cross entropy loss for the main softmax prediction.
  slim.losses.cross_entropy_loss(logits[0],
                                 dense_labels,
                                 label_smoothing=0.1,
                                 weight=1.0)

  # Cross entropy loss for the auxiliary softmax head.
  slim.losses.cross_entropy_loss(logits[1],
                                 dense_labels,
                                 label_smoothing=0.1,
                                 weight=0.4,
                                 scope='aux_loss')