code: def ragged_assert_compatible_and_get_flat_values(values, mask=None):
      """If ragged, it checks the compatibility and then returns the flat_values.
  
         Note: If two tensors are dense, it does not check their compatibility.
         Note: Although two ragged tensors with different ragged ranks could have
               identical overall rank and dimension sizes and hence be compatible,
               we do not support those cases.
      Args:
         values: A list of potentially ragged tensor of the same ragged_rank.
         mask: A potentially ragged tensor of the same ragged_rank as elements in
           Values.
  
      Returns:
         A tuple in which the first element is the list of tensors and the second
         is the mask tensor. ([Values], mask). Mask and the element in Values
         are equal to the flat_values of the input arguments (if they were ragged).
      """
      if isinstance(values, list):
          is_all_ragged = all(
              isinstance(rt, ragged_tensor.RaggedTensor)
              for rt in values
          )
          is_any_ragged = any(
              isinstance(rt, ragged_tensor.RaggedTensor)
              for rt in values
          )
      else:
          is_all_ragged = isinstance(values, ragged_tensor.RaggedTensor)
          is_any_ragged = is_all_ragged
      if is_all_ragged and (
          (mask is None) or isinstance(mask, ragged_tensor.RaggedTensor)
      ):
          to_be_stripped = False
          if not isinstance(values, list):
              values = [values]
              to_be_stripped = True
  
          # NOTE: we leave the flat_values compatibility to
          # tf.TensorShape `assert_is_compatible_with`
          # check if both dynamic dimensions are equal and then use the flat_values.
          nested_row_split_list = [
              rt.nested_row_splits for rt in values
          ]
          assertion_list = _assert_splits_match(nested_row_split_list)
  
          # if both are ragged sample_weights also should be ragged with same dims.
          if isinstance(mask, ragged_tensor.RaggedTensor):
              assertion_list_for_mask = _assert_splits_match(
                  [nested_row_split_list[0], mask.nested_row_splits]
              )
              with ops.control_dependencies(assertion_list_for_mask):
                  mask = array_ops.expand_dims(mask.flat_values, -1)
  
          # values has at least 1 element.
          flat_values = []
          for value in values:
              with ops.control_dependencies(assertion_list):
                  flat_values.append(
                      array_ops.expand_dims(value.flat_values, -1)
                  )
  
          values = flat_values[0] if to_be_stripped else flat_values
  
      elif is_any_ragged:
          raise TypeError(
              "One of the inputs does not have acceptable types."
          )
      # values are empty or value are not ragged and mask is ragged.
      elif isinstance(mask, ragged_tensor.RaggedTensor):
          raise TypeError(
              "Ragged mask is not allowed with non-ragged inputs."
          )
  
      return values, mask
  
imports: from enum import Enum
  import functools
  import weakref
  import numpy
  from tensorflow.python.compat import compat
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.utils import losses_utils
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.keras.utils.generic_utils import to_list
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import clip_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import gen_math_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops import weights_broadcast_ops
  from tensorflow.python.ops.parallel_for import control_flow_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.util import tf_decorator
label_desc: Use image node_id 10 for calling a global function with example usage: ragged_assert_compatible_and_get_flat_values(values, mask) and returns: values,  mask