parameters: (graph, image)
docstring: Classify image using custom inception model
      
      [description]
      
      Arguments:
          graph {graph_def} -- custom inception model
          image {numpy} -- image
      
      Returns:
          int[] -- label probabilities
docstring_summary: Classify image using custom inception model
      
      [description]
      
      Arguments:
          graph {graph_def} -- custom inception model
          image {numpy} -- image
      
      Returns:
          int[] -- label probabilities
docstring_tokens: Classify image using custom inception model [ description ] Arguments : graph { graph_def } -- custom inception model image { numpy } -- image Returns : int [] -- label probabilities
function: def predict(graph, image):
      """Classify image using custom inception model
      
      [description]
      
      Arguments:
          graph {graph_def} -- custom inception model
          image {numpy} -- image
      
      Returns:
          int[] -- label probabilities
      """
      
      with tf.Session(graph=graph) as sess:
          input_tensor = sess.graph.get_tensor_by_name('input_1:0')
          output = sess.run('dense_2/Softmax:0',feed_dict ={input_tensor:image})
          return output
function_tokens: def predict ( graph , image ) : with tf . Session ( graph = graph ) as sess : input_tensor = sess . graph . get_tensor_by_name ( 'input_1:0' ) output = sess . run ( 'dense_2/Softmax:0' , feed_dict = { input_tensor : image } ) return output