class_docstr: Layer to generate mask.
docstr: Generates mask (whether example is valid) from features.
  
  Args:
    inputs: (dict) Features with a mix of context (2D) and example features
      (3D).
  
  Returns:
    mask: (tf.Tensor) Mask is a tensor of shape [batch_size, list_size], which
      is True for a valid example and False for invalid one.
docstr_tok: ["Generates","mask","(","whether","example","is","valid",")","from","features",".","Args",":","inputs",":","(","dict",")","Features","with","a","mix","of","context","(","2D",")","and","example","features","(","3D",")",".","Returns",":","mask",":","(","tf.Tensor",")","Mask","is","a","tensor","of","shape","[","batch_size",",","list_size","]",",","which","is","True","for","a","valid","example","and","False","for","invalid","one","."]
code: def call(self, inputs):
      """Generates mask (whether example is valid) from features.
  
      Args:
        inputs: (dict) Features with a mix of context (2D) and example features
          (3D).
  
      Returns:
        mask: (tf.Tensor) Mask is a tensor of shape [batch_size, list_size], which
          is True for a valid example and False for invalid one.
      """
      example_feature = inputs[
          next(six.iterkeys(self._example_feature_columns))
      ]
      list_size = tf.shape(example_feature)[1]
      sizes = inputs[self._size_feature_name]
      mask = tf.sequence_mask(sizes, maxlen=list_size)
      return mask
  
imports: import six
  import tensorflow.compat.v2
  from tensorflow.python.feature_column import feature_column_lib
  from tensorflow_ranking.python import utils
label_desc: Use image node_id 4 for calling the GenerateMask obj's underlying member method code with example usage: obj.call(inputs) and returns: mask