old_contents: # mpmodel_ue4.py
  #
  # Copyright (c) [2017] [yukirin]
  #
  # This software is released under the MIT License.
  # http://opensource.org/licenses/mit-license.php
  # ==============================================================================
  import tensorflow as tf
  import numpy as np
  
  import unreal_engine as ue
  from TFPluginAPI import TFPluginAPI
  
  import mpmodel
  import reader
  import plot
  
  class MPModelUE4(TFPluginAPI):
     def setup(self):
         eval_config = mpmodel.get_config()
         eval_config.batch_size = 1
         self.num_steps = eval_config.num_steps
         self.output_size = eval_config.output_size
         self.input_size = eval_config.input_size
  
         with tf.Graph().as_default():
             initializer = tf.random_uniform_initializer(-eval_config.init_scale, eval_config.init_scale)
             with tf.name_scope("Test"):
                test_input = mpmodel.MPInput(config=eval_config, data=test_data,
                                             is_predict=True, name="TestInput")
                with tf.variable_scope("Model", reuse=False, initializer=initializer):
                   self.model = mpmodel.MPModel(is_training=False, config=eval_config,
                                                                 input_=test_input, is_test=True)
  
         self.sess = tf.InteractiveSession()
         ckpt = tf.train.get_checkpoint_state('./save')
         last_model = ckpt.model_checkpoint_path
         saver = tf.train.Saver()
         saver.restore(sess, last_model)
  
     def _decodeJson(self, jsonInput):
        data = np.array(jsonInput['input'].split(','), dtype=np.float32)
        data = np.reshape(data, [self.num_steps, self.input_size])
        return data
  
     def _encodeJson(predict_data):
        result = {}
        for i, pos in enumerate(predict_data):
          result[str(i)] = ",".join(str(n) for n in pos)
        return result
  
     def runJsonInput(self, jsonInput):
         input_data = self._decodeJson(jsonInput)
         feed_dict = {"Test/Model/Placeholder:0": input_data}
         return self._encodeJson(self.sess.run(self.model.logits, feed_dict)) 
  
     def train(self):
         pass
  
  def getApi():
      return MPModelUE4.getInstance()
new_contents: # mpmodel_ue4.py
  #
  # Copyright (c) [2017] [yukirin]
  #
  # This software is released under the MIT License.
  # http://opensource.org/licenses/mit-license.php
  # ==============================================================================
  import tensorflow as tf
  import numpy as np
  
  import unreal_engine as ue
  from TFPluginAPI import TFPluginAPI
  
  import mpmodel
  
  class MPModelUE4(TFPluginAPI):
     def setup(self):
         eval_config = mpmodel.get_config()
         eval_config.batch_size = 1
         self.num_steps = eval_config.num_steps
         self.output_size = eval_config.output_size
         self.input_size = eval_config.input_size
  
         with tf.Graph().as_default():
             initializer = tf.random_uniform_initializer(-eval_config.init_scale, eval_config.init_scale)
             with tf.name_scope("Test"):
                test_input = mpmodel.MPInput(config=eval_config, data=test_data,
                                             is_predict=True, name="TestInput")
                with tf.variable_scope("Model", reuse=False, initializer=initializer):
                   self.model = mpmodel.MPModel(is_training=False, config=eval_config,
                                                                 input_=test_input, is_test=True)
  
         self.sess = tf.InteractiveSession()
         ckpt = tf.train.get_checkpoint_state('./save')
         last_model = ckpt.model_checkpoint_path
         saver = tf.train.Saver()
         saver.restore(sess, last_model)
  
     def _decodeJson(self, jsonInput):
        data = np.array(jsonInput['input'].split(','), dtype=np.float32)
        data = np.reshape(data, [self.num_steps, self.input_size])
        return data
  
     def _encodeJson(predict_data):
        result = {}
        for i, pos in enumerate(predict_data):
          result[str(i)] = ",".join(str(n) for n in pos)
        return result
  
     def runJsonInput(self, jsonInput):
         input_data = self._decodeJson(jsonInput)
         feed_dict = {"Test/Model/Placeholder:0": input_data}
         return self._encodeJson(self.sess.run(self.model.logits, feed_dict)) 
  
     def train(self):
         pass
  
  def getApi():
      return MPModelUE4.getInstance()
subject: Fix import