class_docstr: Create a `padding_mask` of `ones` to match the current unpadded image.
code: def __call__(self, features):
      h, w = get_hw(features, dtype=tf.int32)
      # padding_mask is initialized as ones. It will later be padded with zeros.
      features["padding_mask"] = tf.ones((h, w), dtype=tf.float32)
      return features
  
imports: from typing import Any, Dict
  import tensorflow
label_desc: Use image node_id 1 for calling the InitPaddingMask obj's underlying member method code with example usage: obj.__call__(features) and returns: features