code: def get_placeholder(name, dtype, shape):
      if name in _PLACEHOLDER_CACHE:
          out, dtype1, shape1 = _PLACEHOLDER_CACHE[name]
          if out.graph == tf.get_default_graph():
              assert (
                  dtype1 == dtype and shape1 == shape
              ), "Placeholder with name {} has already been registered and has shape {}, different from requested {}".format(
                  name, shape1, shape
              )
              return out
  
      out = tf.placeholder(dtype=dtype, shape=shape, name=name)
      _PLACEHOLDER_CACHE[name] = (out, dtype, shape)
      return out
  
imports: import numpy
  import tensorflow
  import copy
  import os
  import functools
  import collections
  import multiprocessing
label_desc: Use image node_id 17 for calling a global function with example usage: get_placeholder(name, dtype, shape) and returns: out, out