docstr: Export a Dataset whose elements are slices of the given tensors.
  
  :param batch_size: Number of samples per batch of computation.
         If unspecified, batch_size will default to 32.
  
  :return: a tf.data dataset, including x and y.
docstr_tok: ["Export","a","Dataset","whose","elements","are","slices","of","the","given","tensors",".",":","param","batch_size",":","Number","of","samples","per","batch","of","computation",".","If","unspecified",",","batch_size","will","default","to","32.",":","return",":","a","tf.data","dataset",",","including","x","and","y","."]
code: def to_tf_dataset(self, batch_size=32, shuffle=False):
      """
      Export a Dataset whose elements are slices of the given tensors.
  
      :param batch_size: Number of samples per batch of computation.
             If unspecified, batch_size will default to 32.
  
      :return: a tf.data dataset, including x and y.
      """
      # TODO Requires a tf dataset creator method and can be use less memory.
      import tensorflow as tf
      from bigdl.nano.utils.common import invalidInputError
  
      if self.numpy_x is None:
          invalidInputError(
              False,
              "Please call 'roll' method "
              "before transform a TSDataset to tf dataset!",
          )
      data = tf.data.Dataset.from_tensor_slices(
          (self.numpy_x, self.numpy_y)
      )
      batch_size = 32 if batch_size is None else batch_size
      if shuffle:
          data = (
              data.cache()
              .shuffle(self.numpy_x.shape[0])
              .batch(batch_size)
          )
      else:
          data = data.batch(batch_size).cache()
      return data.prefetch(tf.data.AUTOTUNE)
  
imports: import pandas
  import numpy
  import functools
  import logging
  from bigdl.chronos.data.utils.feature import generate_dt_features, generate_global_features
  from bigdl.chronos.data.utils.impute import impute_timeseries_dataframe
  from bigdl.chronos.data.utils.deduplicate import deduplicate_timeseries_dataframe
  from bigdl.chronos.data.utils.roll import roll_timeseries_dataframe
  from bigdl.chronos.data.utils.time_feature import time_features, gen_time_enc_arr
  from bigdl.chronos.data.utils.scale import unscale_timeseries_numpy, scale_timeseries_numpy
  from bigdl.chronos.data.utils.resample import resample_timeseries_dataframe
  from bigdl.chronos.data.utils.split import split_timeseries_dataframe
  from bigdl.chronos.data.utils.cycle_detection import cycle_length_est
  from bigdl.chronos.data.utils.quality_inspection import quality_check_timeseries_dataframe, _abnormal_value_repair
  from bigdl.chronos.data.utils.utils import _to_list, _check_type, _check_col_within, _check_col_no_na, _check_is_aligned, _check_dt_is_sorted
label_desc: Use image node_id 14 for calling the TSDataset obj's underlying member method code with example usage: obj.to_tf_dataset(batch_size, shuffle) and returns: data