parameters: (input_width, input_height, input_depth, input_mean,
                        input_std)
return_statement: return jpeg_data, mul_image
docstring: Adds operations that perform JPEG decoding and resizing to the graph..
  
    Args:
      input_width: Desired width of the image fed into the recognizer graph.
      input_height: Desired width of the image fed into the recognizer graph.
      input_depth: Desired channels of the image fed into the recognizer graph.
      input_mean: Pixel value that should be zero in the image for the graph.
      input_std: How much to divide the pixel values by before recognition.
  
    Returns:
      Tensors for the node to feed JPEG data into, and the output of the
        preprocessing steps.
docstring_summary: Adds operations that perform JPEG decoding and resizing to the graph..
docstring_tokens: Adds operations that perform JPEG decoding and resizing to the graph ..
function: def add_jpeg_decoding(input_width, input_height, input_depth, input_mean,
                        input_std):
    """Adds operations that perform JPEG decoding and resizing to the graph..
  
    Args:
      input_width: Desired width of the image fed into the recognizer graph.
      input_height: Desired width of the image fed into the recognizer graph.
      input_depth: Desired channels of the image fed into the recognizer graph.
      input_mean: Pixel value that should be zero in the image for the graph.
      input_std: How much to divide the pixel values by before recognition.
  
    Returns:
      Tensors for the node to feed JPEG data into, and the output of the
        preprocessing steps.
    """
    jpeg_data = tf.placeholder(tf.string, name='DecodeJPGInput')
    decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)
    decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32)
    decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0)
    resize_shape = tf.stack([input_height, input_width])
    resize_shape_as_int = tf.cast(resize_shape, dtype=tf.int32)
    resized_image = tf.image.resize_bilinear(decoded_image_4d,
                                             resize_shape_as_int)
    offset_image = tf.subtract(resized_image, input_mean)
    mul_image = tf.multiply(offset_image, 1.0 / input_std)
    return jpeg_data, mul_image
function_tokens: def add_jpeg_decoding ( input_width , input_height , input_depth , input_mean , input_std ) : jpeg_data = tf . placeholder ( tf . string , name = 'DecodeJPGInput' ) decoded_image = tf . image . decode_jpeg ( jpeg_data , channels = input_depth ) decoded_image_as_float = tf . cast ( decoded_image , dtype = tf . float32 ) decoded_image_4d = tf . expand_dims ( decoded_image_as_float , 0 ) resize_shape = tf . stack ( [ input_height , input_width ] ) resize_shape_as_int = tf . cast ( resize_shape , dtype = tf . int32 ) resized_image = tf . image . resize_bilinear ( decoded_image_4d , resize_shape_as_int ) offset_image = tf . subtract ( resized_image , input_mean ) mul_image = tf . multiply ( offset_image , 1.0 / input_std ) return jpeg_data , mul_image