class_docstr: Strategy extension that support both single and multi worker strategy.
code: def _gather_to_implementation(
      self, value, destinations, axis, options
  ):
      if isinstance(value, dtensor_util.DTensorDistributedValue):
          value = value.get_dtensor()
      if not d_api.is_dtensor(value):
          # This is the current behavior for mirrored strategy, should we raise an
          # error for unsupported types?
          return value
  
      # Unpack the dtensor components and gather the tensors on the axis
      components = d_api.unpack(value)
      return array_ops.concat(components, axis=axis)
  
imports: import functools
  from tensorflow.dtensor.python import api
  from tensorflow.dtensor.python import config
  from tensorflow.dtensor.python import d_variable
  from tensorflow.dtensor.python import input_util
  from tensorflow.dtensor.python import layout
  from tensorflow.python.data.experimental.ops import distribute
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.distribute import distribute_utils
  from tensorflow.python.distribute.experimental import dtensor_util
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.util import nest
label_desc: Use image node_id 16 for calling the DTensorStrategyExtended obj's underlying member method code with example usage: obj._gather_to_implementation(value, destinations, axis, options) and returns: array_ops, value