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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _get_case_file_paths(tmp_dir, case, training_fraction=0.95):
 """Obtain a list of image paths corresponding to training or eval case. Args: tmp_dir: str, the root path to which raw images were written, at the top level having meta/ and raw/ subdirs. case: bool, whether obtaining file paths for training (true) or eval (false). training_fraction: float, the fraction of the sub-image path list to consider as the basis for training examples. Returns: list: A list of file paths. Raises: ValueError: if images not found in tmp_dir, or if training_fraction would leave no examples for eval. """


  paths = tf.gfile.Glob("%s/*.jpg" % tmp_dir)

  if not paths:
    raise ValueError("Search of tmp_dir (%s) " % tmp_dir,
                     "for subimage paths yielded an empty list, ",
                     "can't proceed with returning training/eval split.")

  split_index = int(math.floor(len(paths)*training_fraction))

  if split_index >= len(paths):
    raise ValueError("For a path list of size %s "
                     "and a training_fraction of %s "
                     "the resulting split_index of the paths list, "
                     "%s, would leave no elements for the eval "
                     "condition." % (len(paths),
                                     training_fraction,
                                     split_index))

  if case:
    return paths[:split_index]
  else:
    return paths[split_index:]