class_docstr: Wrapper for dataset generation given a read function
class_docstr_tok: ["Wrapper","for","dataset","generation","given","a","read","function"]
docstr: Build the serving inputs.
  
  Args:
      placeholder_shapes: A nested structure of lists or tuples
          corresponding to the shape of each component of the feature
          elements yieled by the read_fn.
  
  Returns:
      function: A function to be passed to the tf.estimator.Estimator
      instance when exporting a saved model with estimator.export_savedmodel.
docstr_tok: ["Build","the","serving","inputs",".","Args",":","placeholder_shapes",":","A","nested","structure","of","lists","or","tuples","corresponding","to","the","shape","of","each","component","of","the","feature","elements","yieled","by","the","read_fn",".","Returns",":","function",":","A","function","to","be","passed","to","the","tf.estimator.Estimator","instance","when","exporting","a","saved","model","with","estimator.export_savedmodel","."]
code: def serving_input_receiver_fn(self, placeholder_shapes):
      """Build the serving inputs.
  
      Args:
          placeholder_shapes: A nested structure of lists or tuples
              corresponding to the shape of each component of the feature
              elements yieled by the read_fn.
  
      Returns:
          function: A function to be passed to the tf.estimator.Estimator
          instance when exporting a saved model with estimator.export_savedmodel.
      """
  
      def f():
          inputs = {
              k: tf.placeholder(
                  shape=[None]
                  + list(placeholder_shapes["features"][k]),
                  dtype=self.dtypes["features"][k],
              )
              for k in list(self.dtypes["features"].keys())
          }
  
          return tf.estimator.export.ServingInputReceiver(
              inputs, inputs
          )
  
      return f
  
file_path: DLTK/dltk/io/abstract_reader.py
imports: from __future__ import unicode_literals
  from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import tensorflow
  import traceback
label_desc: Use image node_id 3 for calling the Reader obj's underlying member method code with example usage: obj.serving_input_receiver_fn(placeholder_shapes) and returns: f, tf