code: def _tag_sharding_attribute_for_dequeued_tensor(tensor, dims):
      """Tags appropriate XLA sharding attribute to the dequeued tensor.
  
      The sharding attribute of the dequeued tensor will be a tuple.
  
      Args:
        tensor: The dequeued tensor on TPU.
        dims: A list of integer describes how the tensor is partitioned.
  
      Returns:
        The same tensor with the xla_sharding attribute.
      """
      if dims is None:
          return xla_sharding.replicate(
              tensor, assign_tuple_sharding=True
          )
      elif np.prod(dims) == 1:
          return xla_sharding.assign_device(
              tensor, 0, assign_tuple_sharding=True
          )
      else:
          tile_assignment = np.arange(np.prod(dims)).reshape(dims)
          return xla_sharding.tile(
              tensor=tensor,
              tile_assignment=tile_assignment,
              assign_tuple_sharding=True,
          )
  
imports: import itertools
  import numpy
  from tensorflow.python.compiler.xla.experimental import xla_sharding
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.tpu import tpu_name_util
  from tensorflow.python.tpu import tpu_sharding
  from tensorflow.python.tpu.ops import tpu_ops
  from tensorflow.python.util import nest
label_desc: Use image node_id 2 for calling a global function with example usage: _tag_sharding_attribute_for_dequeued_tensor(tensor, dims) and returns: xla_sharding, xla_sharding, xla_sharding