code: def function(inputs, outputs, updates=None, givens=None):
      """
      Take a bunch of tensorflow placeholders and expressions
      computed based on those placeholders and produces f(inputs) -> outputs. Function f takes
      values to be fed to the input's placeholders and produces the values of the expressions
      in outputs. Just like a Theano function.
  
      Input values can be passed in the same order as inputs or can be provided as kwargs based
      on placeholder name (passed to constructor or accessible via placeholder.op.name).
  
      Example:
         >>> x = tf.placeholder(tf.int32, (), name="x")
         >>> y = tf.placeholder(tf.int32, (), name="y")
         >>> z = 3 * x + 2 * y
         >>> lin = function([x, y], z, givens={y: 0})
         >>> with single_threaded_session():
         >>>     initialize()
         >>>     assert lin(2) == 6
         >>>     assert lin(x=3) == 9
         >>>     assert lin(2, 2) == 10
  
      :param inputs: (TensorFlow Tensor or Object with make_feed_dict) list of input arguments
      :param outputs: (TensorFlow Tensor) list of outputs or a single output to be returned from function. Returned
          value will also have the same shape.
      :param updates: ([tf.Operation] or tf.Operation)
          list of update functions or single update function that will be run whenever
          the function is called. The return is ignored.
      :param givens: (dict) the values known for the output
      """
      if isinstance(outputs, list):
          return _Function(inputs, outputs, updates, givens=givens)
      elif isinstance(outputs, (dict, collections.OrderedDict)):
          func = _Function(
              inputs, outputs.values(), updates, givens=givens
          )
          return lambda *args, **kwargs: type(outputs)(
              zip(outputs.keys(), func(*args, **kwargs))
          )
      else:
          func = _Function(inputs, [outputs], updates, givens=givens)
          return lambda *args, **kwargs: func(*args, **kwargs)[0]
  
imports: import os
  import collections
  import functools
  import multiprocessing
  from typing import Set
  import numpy
  import tensorflow
label_desc: Use image node_id 16 for calling a global function with example usage: function(inputs, outputs, updates, givens) and returns: _Function, function, function