code: def estimate_size(cls, ctx, op):
      if op.stage == OperandStage.map:
          fancy_index_size = len(op.axes)
          inp_size = ctx[op.inputs[0].key][0]
          factor = (
              1 / float(fancy_index_size) + fancy_index_size
          )  # 1/#fancy_index is the poses
          ctx[op.outputs[0].key] = (inp_size * factor,) * 2
      else:
          sum_size = 0
          for shuffle_input in op.inputs[0].inputs or ():
              sum_size += ctx[shuffle_input.key]
          for out_chunk in op.outputs:
              ctx[out_chunk.key] = sum_size, sum_size
  
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 9 for calling the FancyIndexingDistribute obj's underlying member method code with example usage: obj.estimate_size(cls, ctx, op) without return types