code: def _execute_map(cls, ctx, op):
      nsplits = op.dest_nsplits
      axes = op.axes
      fancy_index_nsplits = [nsplits[ax] for ax in axes]
      indexes = ctx[op.inputs[0].key]
      flatten_indexes = indexes.reshape(indexes.shape[0], -1)
      idx_to_fancy_indexes, idx_to_poses = split_indexes_into_chunks(
          fancy_index_nsplits, flatten_indexes, False
      )
      for idx in idx_to_fancy_indexes:
          ctx[op.outputs[0].key, idx] = (
              idx_to_fancy_indexes[idx],
              idx_to_poses[idx],
          )
  
imports: from numbers import Integral
  import numpy
  from ...None import opcodes
  from ...core import ENTITY_TYPE
  from ...core.operand import OperandStage
  from ...serialization.serializables import FieldTypes, KeyField, ListField, TupleField, Int32Field
  from ..core import TENSOR_TYPE, TensorOrder
  from ..utils import split_indexes_into_chunks, calc_pos, filter_inputs
  from ..operands import TensorHasInput, TensorOperandMixin, TensorMapReduceOperand
  from ..array_utils import get_array_module
  from .core import process_index, calc_shape
  from .index_lib import TensorIndexesHandler
label_desc: Use image node_id 6 for calling the FancyIndexingDistribute obj's underlying member method code with example usage: obj._execute_map(cls, ctx, op) without return types