code: def get_mask_values(self, masked_lm_ids, **kwargs):
      # If we're expecting all random tokens, set to all [RANDOM]
      if self.random_token_rate == 1:
          flat_mask_values = array_ops.tile(
              array_ops.expand_dims(self._random_token, -1),
              array_ops.shape(masked_lm_ids.flat_values),
          )
          flat_mask_values = math_ops.cast(
              flat_mask_values, dtypes.int64
          )
      else:
          # Give them all [MASK] values.
          flat_mask_values = array_ops.tile(
              array_ops.expand_dims(self.mask_token, -1),
              array_ops.shape(masked_lm_ids.flat_values),
          )
          flat_mask_values = math_ops.cast(
              flat_mask_values, dtypes.int64
          )
      return masked_lm_ids.with_flat_values(flat_mask_values)
  
imports: from absl.testing import parameterized
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import lookup_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.ragged import ragged_factory_ops
  from tensorflow.python.ops.ragged import ragged_functional_ops
  from tensorflow.python.platform import test
  from tensorflow_text.python.ops import item_selector_ops
  from tensorflow_text.python.ops import masking_ops
label_desc: Use image node_id 2 for calling the AlwaysRandomValuesChooser obj's underlying member method code with example usage: obj.get_mask_values(masked_lm_ids) and returns: masked_lm_ids