class_docstr: Base class of Input processor.
class_docstr_tok: ["Base","class","of","Input","processor","."]
docstr: Initializes a new `InputProcessor`.
  
  Args:
    image: The input image before processing.
    output_size: The output image size after calling resize_and_crop_image
      function.
docstr_tok: ["Initializes","a","new","`","InputProcessor","`",".","Args",":","image",":","The","input","image","before","processing",".","output_size",":","The","output","image","size","after","calling","resize_and_crop_image","function","."]
code: def __init__(self, image, output_size):
      """Initializes a new `InputProcessor`.
  
      Args:
        image: The input image before processing.
        output_size: The output image size after calling resize_and_crop_image
          function.
      """
      self._image = image
      if isinstance(output_size, int):
          self._output_size = (output_size, output_size)
      else:
          self._output_size = output_size
      # Parameters to control rescaling and shifting during preprocessing.
      # Image scale defines scale from original image to scaled image.
      self._image_scale = tf.constant(1.0)
      # The integer height and width of scaled image.
      self._scaled_height = tf.shape(image)[0]
      self._scaled_width = tf.shape(image)[1]
      # The x and y translation offset to crop scaled image to the output size.
      self._crop_offset_y = tf.constant(0)
      self._crop_offset_x = tf.constant(0)
  
imports: from absl import logging
  import tensorflow
  import utils
  from pipeline import anchors
  from .None import preprocessor
  from .None import tf_example_decoder
label_desc: Use image node_id 1 to create a new InputProcessor object  with example: obj = InputProcessor(image, output_size)