code: def _reduce_across_tensors(reduce_fn, input_tensors):
      """Applies a reduce_fn across a list of tensors on non-batch dimensions."""
      reduced_within_tensor = [
          reduce_fn(t, axis=list(range(1, t.shape.rank)))
          for t in input_tensors
      ]
      if len(input_tensors) == 1:
          return reduced_within_tensor[0]
      return reduce_fn(
          tf.stack(reduced_within_tensor, axis=-1), axis=-1
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import neural_structured_learning.configs
  import six
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: _reduce_across_tensors(reduce_fn, input_tensors) and returns: reduce_fn, reduced_within_tensor