message: Make non-iterable input to `stratified_sample` produce better error message.
diff: @@ -150,7 +150,7 @@ def stratified_sample(tensors,
  tensors: List of tensors for data. All tensors are either one item or a
  batch, according to enqueue_many.
  labels: Tensor for label of data. Label is a single integer or a batch,
  - depending on enqueue_many. It is not a one-hot vector.
  + depending on `enqueue_many`. It is not a one-hot vector.
  target_probs: Target class proportions in batch. An object whose type has a
  registered Tensor conversion function.
  batch_size: Size of batch to be returned.
  @@ -164,9 +164,10 @@ def stratified_sample(tensors,
  examples and for the final queue with the proper class proportions.
  name: Optional prefix for ops created by this function.
  Raises:
  - ValueError: enqueue_many is True and labels doesn't have a batch
  - dimension, or if enqueue_many is False and labels isn't a scalar.
  - ValueError: enqueue_many is True, and batch dimension on data and labels
  + ValueError: If `tensors` isn't iterable.
  + ValueError: `enqueue_many` is True and labels doesn't have a batch
  + dimension, or if `enqueue_many` is False and labels isn't a scalar.
  + ValueError: `enqueue_many` is True, and batch dimension on data and labels
  don't match.
  ValueError: if probs don't sum to one.
  ValueError: if a zero initial probability class has a nonzero target
  @@ -188,7 +189,7 @@ def stratified_sample(tensors,
  # Run batch through network.
  ...
  """
  - with ops.name_scope(name, 'stratified_sample', tensors + [labels]):
  + with ops.name_scope(name, 'stratified_sample', list(tensors) + [labels]):
  tensor_list = ops.convert_n_to_tensor_or_indexed_slices(tensors)
  labels = ops.convert_to_tensor(labels)
  target_probs = ops.convert_to_tensor(target_probs, dtype=dtypes.float32)
  