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+### `tf.train.batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, shared_name=None, name=None)` {#batch_join}
+
+Runs a list of tensors to fill a queue to create batches of examples.
+
+The `tensors_list` argument is a list of tuples of tensors, or a list of
+dictionaries of tensors.  Each element in the list is treated similarily
+to the `tensors` argument of `tf.train.batch()`.
+
+Enqueues a different list of tensors in different threads.
+Implemented using a queue -- a `QueueRunner` for the queue
+is added to the current `Graph`'s `QUEUE_RUNNER` collection.
+
+`len(tensors_list)` threads will be started,
+with thread `i` enqueuing the tensors from
+`tensors_list[i]`. `tensors_list[i1][j]` must match
+`tensors_list[i2][j]` in type and shape, except in the first
+dimension if `enqueue_many` is true.
+
+If `enqueue_many` is `False`, each `tensors_list[i]` is assumed
+to represent a single example. An input tensor `x` will be output as a
+tensor with shape `[batch_size] + x.shape`.
+
+If `enqueue_many` is `True`, `tensors_list[i]` is assumed to
+represent a batch of examples, where the first dimension is indexed
+by example, and all members of `tensors_list[i]` should have the
+same size in the first dimension.  The slices of any input tensor
+`x` are treated as examples, and the output tensors will have shape
+`[batch_size] + x.shape[1:]`.
+
+The `capacity` argument controls the how long the prefetching is allowed to
+grow the queues.
+
+The returned operation is a dequeue operation and will throw
+`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
+operation is feeding another input queue, its queue runner will catch
+this exception, however, if this operation is used in your main thread
+you are responsible for catching this yourself.
+
+*N.B.:* If `dynamic_pad` is `False`, you must ensure that either
+(i) the `shapes` argument is passed, or (ii) all of the tensors in
+`tensors_list` must have fully-defined shapes. `ValueError` will be
+raised if neither of these conditions holds.
+
+If `dynamic_pad` is `True`, it is sufficient that the *rank* of the
+tensors is known, but individual dimensions may have value `None`.
+In this case, for each enqueue the dimensions with value `None`
+may have a variable length; upon dequeue, the output tensors will be padded
+on the right to the maximum shape of the tensors in the current minibatch.
+For numbers, this padding takes value 0.  For strings, this padding is
+the empty string.  See `PaddingFIFOQueue` for more info.
+
+##### Args:
+
+
+*  <b>`tensors_list`</b>: A list of tuples or dictionaries of tensors to enqueue.
+*  <b>`batch_size`</b>: An integer. The new batch size pulled from the queue.
+*  <b>`capacity`</b>: An integer. The maximum number of elements in the queue.
+*  <b>`enqueue_many`</b>: Whether each tensor in `tensor_list_list` is a single
+    example.
+*  <b>`shapes`</b>: (Optional) The shapes for each example.  Defaults to the
+    inferred shapes for `tensor_list_list[i]`.
+*  <b>`dynamic_pad`</b>: Boolean.  Allow variable dimensions in input shapes.
+    The given dimensions are padded upon dequeue so that tensors within a
+    batch have the same shapes.
+*  <b>`shared_name`</b>: (Optional) If set, this queue will be shared under the given
+    name across multiple sessions.
+*  <b>`name`</b>: (Optional) A name for the operations.
+
+##### Returns:
+
+  A list or dictionary of tensors with the same number and types as
+  `tensors_list[i]`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be
+    inferred from the elements of `tensor_list_list`.
+