code: def _execute_map(cls, ctx, op):
      indexed_array = ctx[op.inputs[0].key]
      sizes, pos = ctx[op.inputs[1].key]
      acc_sizes = np.cumsum(sizes)
      fancy_index_axis = op.fancy_index_axis
  
      for i in range(len(sizes)):
          start = 0 if i == 0 else acc_sizes[i - 1]
          end = acc_sizes[i]
          select = (slice(None),) * fancy_index_axis + (
              slice(start, end),
          )
          ctx[op.outputs[0].key, (i,)] = (
              indexed_array[select],
              pos[start:end],
          )
  
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 5 for calling the FancyIndexingConcat obj's underlying member method code with example usage: obj._execute_map(cls, ctx, op) without return types