class_docstr: A fake image data provider.
docstr: See `data.Provider` get_input_fn.
code: def get_input_fn(self, partition, mode, batch_size):
      """See `data.Provider` get_input_fn."""
  
      del partition
  
      def input_fn(params=None):
          """Input_fn to return."""
  
          del params  # Unused.
  
          np.random.seed(self._seed)
          if mode == tf.estimator.ModeKeys.EVAL:
              np.random.seed(self._seed + 1)
  
          images = tf.to_float(
              tf.convert_to_tensor(
                  np.random.rand(self._num_examples, *self._shape())
              )
          )
          labels = tf.convert_to_tensor(
              np.random.randint(0, high=2, size=(self._num_examples, 1))
          )
          dataset = tf.data.Dataset.from_tensor_slices(
              ({"x": images}, labels)
          )
          if mode == tf.estimator.ModeKeys.TRAIN:
              dataset = dataset.repeat()
          dataset = dataset.batch(batch_size)
          iterator = dataset.make_one_shot_iterator()
          return iterator.get_next()
  
      return input_fn
  
file_path: adanet/research/improve_nas/trainer/fake_data.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling the FakeImageProvider obj's underlying member method code with example usage: obj.get_input_fn(partition, mode, batch_size) and returns: input_fn, iterator