docstr: Test `.flat_map().skip()` checkpointing behavior.
  
  `SkipInternal` and `GetNextInternal` are separate functions
  but with slighly different implementations.
  Therefore, we should test this op's behavior when used with `.skip()`.
  
  Args:
    verify_fn: Verify the correctness of this dataset's checkpointing.
    symbolic_checkpoint: Whether symbolic checkpointing is turned on.
    num_skips: `.skip(num_skips)`
docstr_tok: ["Test","`",".flat_map","(",")",".skip","(",")","`","checkpointing","behavior",".","`","SkipInternal","`","and","`","GetNextInternal","`","are","separate","functions","but","with","slighly","different","implementations",".","Therefore",",","we","should","test","this","op","'s","behavior","when","used","with","`",".skip","(",")","`",".","Args",":","verify_fn",":","Verify","the","correctness","of","this","dataset","'s","checkpointing",".","symbolic_checkpoint",":","Whether","symbolic","checkpointing","is","turned","on",".","num_skips",":","`",".skip","(","num_skips",")","`"]
code: def testWithSkip(self, verify_fn, symbolic_checkpoint, num_skips):
      """Test `.flat_map().skip()` checkpointing behavior.
  
      `SkipInternal` and `GetNextInternal` are separate functions
      but with slighly different implementations.
      Therefore, we should test this op's behavior when used with `.skip()`.
  
      Args:
        verify_fn: Verify the correctness of this dataset's checkpointing.
        symbolic_checkpoint: Whether symbolic checkpointing is turned on.
        num_skips: `.skip(num_skips)`
      """
  
      def build_dataset():
          def my_map(x):
              if x == 0:
                  return dataset_ops.Dataset.from_tensor_slices(
                      [0, 1, 2, 3]
                  )
              elif x == 1:
                  return dataset_ops.Dataset.from_tensor_slices(
                      [4, 5, 6, 7]
                  )
              else:
                  return dataset_ops.Dataset.from_tensor_slices(
                      [8, 9, 10, 11]
                  )
  
          indices = dataset_ops.Dataset.from_tensor_slices([0, 1, 2])
          dataset = indices.flat_map(my_map)
          # Skip some elements
          dataset = dataset.skip(num_skips)
  
          options = options_lib.Options()
          options.experimental_symbolic_checkpoint = symbolic_checkpoint
          return dataset.with_options(options)
  
      verify_fn(self, build_dataset, num_outputs=3 * 4 - num_skips)
  
imports: import random
  from absl.testing import parameterized
  import numpy
  from tensorflow.python.client import session
  from tensorflow.python.data.kernel_tests import checkpoint_test_base
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import options
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import function
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops.ragged import ragged_conversion_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.platform import test
  from tensorflow.python.training import server_lib
label_desc: Use image node_id 8 for calling the FlatMapCheckpointTest obj's underlying member method code with example usage: obj.testWithSkip(verify_fn, symbolic_checkpoint, num_skips) and returns: dataset, dataset_ops, dataset_ops, dataset_ops