Method flat_map

tensorflow/python/data/ops/dataset_ops.py:1269–1293

Maps `map_func` across this dataset and flattens the result. Use `flat_map` if you want to make sure that the order of your dataset stays the same. For example, to flatten a dataset of batches into a dataset of their elements: ```python a = Dataset.from_tensor_slices([ [1, 2, 3

(self, map_func)

          self, map_func, num_parallel_calls, preserve_cardinality=True)

  def flat_map(self, map_func):
    """Maps `map_func` across this dataset and flattens the result.

    Use `flat_map` if you want to make sure that the order of your dataset
    stays the same. For example, to flatten a dataset of batches into a
    dataset of their elements:

    ```python
    a = Dataset.from_tensor_slices([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ])

    a.flat_map(lambda x: Dataset.from_tensor_slices(x + 1)) # ==>
    #  [ 2, 3, 4, 5, 6, 7, 8, 9, 10 ]
    ```

    `tf.data.Dataset.interleave()` is a generalization of `flat_map`, since
    `flat_map` produces the same output as
    `tf.data.Dataset.interleave(cycle_length=1)`

    Args:
      map_func: A function mapping a dataset element to a dataset.

    Returns:
      Dataset: A `Dataset`.
    """
    return FlatMapDataset(self, map_func)

  def interleave(self,
                 map_func,