code: def valid_slices(draw, batch_shape):
      """Samples a legal (possibly empty) slice for shape batch_shape."""
      # We build up a list of slices in several stages:
      # 1. Choose 0 to batch_rank slices to come before an Ellipsis (...).
      # 2. Decide whether or not to add an Ellipsis; if using, updating the indexing
      #    used (e.g. batch_shape[i]) to identify safe bounds.
      # 3. Choose 0 to [remaining_dims] slices to come last.
      # 4. Decide where to insert between 0 and 3 newaxis slices.
      batch_shape = tf.TensorShape(batch_shape).as_list()
      slices = []
      batch_rank = len(batch_shape)
      arbitrary_slices = hps.tuples(
          hps.one_of(
              hps.just(None),
              hps.integers(min_value=-100, max_value=100),
          ),
          hps.one_of(
              hps.just(None),
              hps.integers(min_value=-100, max_value=100),
          ),
          hps.one_of(
              hps.just(None),
              hps.integers(min_value=-100, max_value=100).filter(
                  lambda x: x != 0
              ),
          ),
      ).map(lambda tup: slice(*tup))
  
      # 1. Choose 0 to batch_rank slices to come before an Ellipsis (...).
      nslc_before_ellipsis = draw(
          hps.integers(min_value=0, max_value=batch_rank)
      )
      for i in range(nslc_before_ellipsis):
          slc = draw(
              hps.one_of(
                  hps.integers(
                      min_value=0, max_value=batch_shape[i] - 1
                  ),
                  arbitrary_slices,
              )
          )
          slices.append(slc)
      # 2. Decide whether or not to add an Ellipsis; if using, updating the indexing
      #    used (e.g. batch_shape[i]) to identify safe bounds.
      has_ellipsis = draw(hps.booleans().map(lambda x: (Ellipsis, x)))[
          1
      ]
      nslc_after_ellipsis = draw(
          hps.integers(
              min_value=0, max_value=batch_rank - nslc_before_ellipsis
          )
      )
      if has_ellipsis:
          slices.append(Ellipsis)
          remain_start, remain_end = (
              batch_rank - nslc_after_ellipsis,
              batch_rank,
          )
      else:
          remain_start = nslc_before_ellipsis
          remain_end = nslc_before_ellipsis + nslc_after_ellipsis
      # 3. Choose 0 to [remaining_dims] slices to come last.
      for i in range(remain_start, remain_end):
          slc = draw(
              hps.one_of(
                  hps.integers(
                      min_value=0, max_value=batch_shape[i] - 1
                  ),
                  arbitrary_slices,
              )
          )
          slices.append(slc)
      # 4. Decide where to insert between 0 and 3 newaxis slices.
      newaxis_positions = draw(
          hps.lists(
              hps.integers(min_value=0, max_value=len(slices)),
              max_size=3,
          )
      )
      for i in sorted(newaxis_positions, reverse=True):
          slices.insert(i, tf.newaxis)
      slices = tuple(slices)
      # Since `d[0]` ==> `d.__getitem__(0)` instead of `d.__getitem__((0,))`;
      # and similarly `d[:3]` ==> `d.__getitem__(slice(None, 3))` instead of
      # `d.__getitem__((slice(None, 3),))`; it is useful to test such scenarios.
      if len(slices) == 1 and draw(hps.booleans()):
          # Sometimes only a single item non-tuple.
          return slices[0]
      return slices
  
imports: import contextlib
  import os
  import re
  import traceback
  import unittest
  import hypothesis
  from hypothesis.extra import numpy
  import hypothesis.strategies
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.util.deferred_tensor import DeferredTensor
label_desc: Use image node_id 30 for calling a global function with example usage: valid_slices(draw, batch_shape) and returns: slices, slices