code: def shuffle_batch_join(
      tensors_list,
      batch_size,
      capacity,
      min_after_dequeue,
      seed=None,
      enqueue_many=False,
      shapes=None,
      allow_smaller_final_batch=False,
      shared_name=None,
      name=None,
  ):
      """Create batches by randomly shuffling tensors.
  
      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 similarly
      to the `tensors` argument of `tf.compat.v1.train.shuffle_batch()`.
  
      This version enqueues a different list of tensors in different threads.
      It adds the following to the current `Graph`:
  
      * A shuffling queue into which tensors from `tensors_list` are enqueued.
      * A `dequeue_many` operation to create batches from the queue.
      * A `QueueRunner` to `QUEUE_RUNNER` collection, to enqueue the tensors
        from `tensors_list`.
  
      `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 with shape `[x, y, z]`
      will be output as a tensor with shape `[batch_size, x, y, z]`.
  
      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.  If an input tensor has shape `[*, x,
      y, z]`, the output will have shape `[batch_size, x, y, z]`.
  
      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.
  
      If `allow_smaller_final_batch` is `True`, a smaller batch value than
      `batch_size` is returned when the queue is closed and there are not enough
      elements to fill the batch, otherwise the pending elements are discarded.
      In addition, all output tensors' static shapes, as accessed via the
      `shape` property will have a first `Dimension` value of `None`, and
      operations that depend on fixed batch_size would fail.
  
      Args:
        tensors_list: A list of tuples or dictionaries of tensors to enqueue.
        batch_size: An integer. The new batch size pulled from the queue.
        capacity: An integer. The maximum number of elements in the queue.
        min_after_dequeue: Minimum number elements in the queue after a
          dequeue, used to ensure a level of mixing of elements.
        seed: Seed for the random shuffling within the queue.
        enqueue_many: Whether each tensor in `tensor_list_list` is a single
          example.
        shapes: (Optional) The shapes for each example.  Defaults to the
          inferred shapes for `tensors_list[i]`.
        allow_smaller_final_batch: (Optional) Boolean. If `True`, allow the final
          batch to be smaller if there are insufficient items left in the queue.
        shared_name: (optional). If set, this queue will be shared under the given
          name across multiple sessions.
        name: (Optional) A name for the operations.
  
      Returns:
        A list or dictionary of tensors with the same number and types as
        `tensors_list[i]`.
  
      Raises:
        ValueError: If the `shapes` are not specified, and cannot be
          inferred from the elements of `tensors_list`.
  
      @compatibility(eager)
      Input pipelines based on Queues are not supported when eager execution is
      enabled. Please use the `tf.data` API to ingest data under eager execution.
      @end_compatibility
      """
      return _shuffle_batch_join(
          tensors_list,
          batch_size,
          capacity,
          min_after_dequeue,
          keep_input=True,
          seed=seed,
          enqueue_many=enqueue_many,
          shapes=shapes,
          allow_smaller_final_batch=allow_smaller_final_batch,
          shared_name=shared_name,
          name=name,
      )
  
imports: from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.layers import utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_assert
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import data_flow_ops
  from tensorflow.python.ops import io_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops import variable_v1
  from tensorflow.python.summary import summary
  from tensorflow.python.training import queue_runner
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.compat import collections_abc
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 34 for calling a global function with example usage: shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, seed, enqueue_many, shapes, allow_smaller_final_batch, shared_name, name) and returns: _shuffle_batch_join