code: def _calc_params(self, params):
      axes = set(self.axes)
      for i, output_type, param in zip(
          itertools.count(0), self.output_types, params
      ):
          if output_type == OutputType.dataframe:
              if 0 in axes:
                  param["index_value"] = self._shuffle_index_value(
                      param["index_value"]
                  )
              if 1 in axes:
                  dtypes = param["dtypes"] = self._shuffle_dtypes(
                      param["dtypes"]
                  )
                  param["columns_value"] = parse_index(
                      dtypes.index, store_data=True
                  )
          elif output_type == OutputType.series:
              if 0 in axes:
                  param["index_value"] = self._shuffle_index_value(
                      param["index_value"]
                  )
          param["_position_"] = i
      return params
  
imports: import itertools
  from collections import defaultdict
  from collections.abc import Iterable
  from functools import reduce
  import numpy
  import pandas
  from ...None import opcodes
  from ...core import get_output_types, recursive_tile
  from ...core.operand import OperandStage, MapReduceOperand
  from ...dataframe.utils import parse_index
  from ...lib import sparse
  from ...serialization.serializables import FieldTypes, TupleField, KeyField
  from ...tensor.utils import validate_axis, check_random_state, gen_random_seeds, decide_unify_split
  from ...tensor.array_utils import get_array_module
  from ...utils import tokenize, lazy_import, has_unknown_shape
  from ...core import ExecutableTuple
  from ..operands import LearnOperandMixin, OutputType, LearnShuffleProxy
  from ..utils import convert_to_tensor_or_dataframe
label_desc: Use image node_id 11 for calling the LearnShuffle obj's underlying member method code with example usage: obj._calc_params(params) and returns: params