code: def calc_paritions_info(cls, op, kth, size, sort_info_chunks):
      # stage5, collect sort infos and calculate partition info for each partitions
      if isinstance(kth, TENSOR_TYPE):
          kth = kth.chunks[0]
          is_kth_input = True
      else:
          is_kth_input = False
      calc_op = CalcPartitionsInfo(
          kth=kth, size=size, dtype=np.dtype(np.int32), gpu=op.gpu
      )
      kws = []
      for i, sort_info_chunk in enumerate(sort_info_chunks):
          kws.append(
              {
                  "shape": sort_info_chunk.shape + (len(kth),),
                  "order": sort_info_chunk.order,
                  "index": sort_info_chunk.index,
                  "pos": i,
              }
          )
      inputs = list(sort_info_chunks)
      if is_kth_input:
          inputs.insert(0, kth)
      return calc_op.new_chunks(inputs, kws=kws, output_limit=len(kws))
  
imports: import itertools
  import numpy
  from ...None import opcodes
  from ...core import recursive_tile
  from ...serialization.serializables import FieldTypes, KeyField, Int32Field, StringField, ListField, BoolField, AnyField
  from ...utils import has_unknown_shape, flatten, stack_back
  from ...core import ENTITY_TYPE, ExecutableTuple
  from ..operands import TensorOperand, TensorShuffleProxy
  from ..core import TENSOR_TYPE, TENSOR_CHUNK_TYPE, TensorOrder
  from ..array_utils import as_same_device, device
  from ..datasource import tensor
  from ..utils import validate_axis, validate_order
  from .psrs import TensorPSRSOperandMixin
label_desc: Use image node_id 1 for calling the ParallelPartitionMixin obj's underlying member method code with example usage: obj.calc_paritions_info(cls, op, kth, size, sort_info_chunks) and returns: calc_op