code: def _execute_agg_reduce(cls, ctx, op: "TensorUnique"):
      input_indexes, input_data = zip(*list(op.iter_mapper_data(ctx)))
  
      inputs = list(zip(*input_data))
      flatten, device_id, xp = as_same_device(
          list(itertools.chain(*inputs)),
          device=op.device,
          ret_extra=True,
      )
      n_ret = len(inputs[0])
      inputs = [
          flatten[i * n_ret : (i + 1) * n_ret]
          for i in range(len(inputs))
      ]
  
      inputs_iter = iter(inputs)
      unique_arrays = next(inputs_iter)
      indices_arrays = next(inputs_iter) if op.return_index else None
      inverse_arrays = next(inputs_iter) if op.return_inverse else None
      counts_arrays = next(inputs_iter) if op.return_counts else None
  
      with device(device_id):
          ar = xp.concatenate(unique_arrays, axis=op.axis)
          result_return_inverse = op.return_inverse or op.return_counts
          axis = op.axis
          if ar.size == 0 or ar.shape[axis] == 0:
              # empty array on the axis
              results = [xp.empty(ar.shape)]
              i = 1
              for it in (
                  op.return_index,
                  op.return_inverse,
                  op.return_counts,
              ):
                  if it:
                      results.append(
                          xp.empty([], dtype=op.outputs[i].dtype)
                      )
                      i += 1
              results = tuple(results)
          else:
              results = xp.unique(
                  ar,
                  return_index=op.return_index,
                  return_inverse=result_return_inverse,
                  axis=axis,
              )
          results = (
              (results,) if not isinstance(results, tuple) else results
          )
          results_iter = iter(results)
          outputs_iter = iter(op.outputs)
          # unique array
          ctx[next(outputs_iter).key] = next(results_iter)
  
          if op.output_limit == 1:
              return
  
          # calc indices
          if op.return_index:
              ctx[next(outputs_iter).key] = xp.concatenate(
                  indices_arrays
              )[next(results_iter)]
          # calc inverse
          try:
              inverse_result = next(results_iter)
              if op.return_inverse:
                  unique_sizes = tuple(
                      ua.shape[op.axis] for ua in unique_arrays
                  )
                  cum_unique_sizes = np.cumsum((0,) + unique_sizes)
                  indices_out_key = next(outputs_iter).key
                  for i, inverse_array in enumerate(inverse_arrays):
                      p = inverse_result[
                          cum_unique_sizes[i] : cum_unique_sizes[i + 1]
                      ]
                      r = xp.empty(
                          inverse_array.shape, dtype=inverse_array.dtype
                      )
                      if inverse_array.size > 0:
                          r[0] = inverse_array[0]
                          r[1] = p[inverse_array[1]]
                      # return unique length and
                      ctx[
                          indices_out_key, (input_indexes[i][op.axis],)
                      ] = (
                          results[0].shape[op.axis],
                          r,
                      )
              # calc counts
              if op.return_counts:
                  result_counts = xp.zeros(
                      results[0].shape[op.axis], dtype=int
                  )
                  t = np.stack(
                      [inverse_result, np.concatenate(counts_arrays)]
                  )
  
                  def acc(a):
                      i, v = a
                      result_counts[i] += v
  
                  np.apply_along_axis(acc, 0, t)
                  ctx[next(outputs_iter).key] = xp.asarray(
                      result_counts
                  )
          except StopIteration:
              pass
  
imports: import itertools
  import numpy
  from ...None import opcodes
  from ...config import options
  from ...core import recursive_tile
  from ...core.operand import OperandStage
  from ...lib import sparse
  from ...lib.sparse.core import get_array_module
  from ...serialization.serializables import BoolField, Int32Field, Int64Field
  from ...utils import has_unknown_shape
  from ..operands import TensorMapReduceOperand, TensorOperandMixin, TensorShuffleProxy
  from ..array_utils import as_same_device, device
  from ..core import TensorOrder
  from ..utils import validate_axis, hash_on_axis
label_desc: Use image node_id 15 for calling the TensorUnique obj's underlying member method code with example usage: obj._execute_agg_reduce(cls, ctx, op) without return types