code: def decode(obj):
      """
      Decoder for deserializing numpy data types.
      """
  
      typ = obj.get("typ")
      if typ is None:
          return obj
      elif typ == "timestamp":
          freq = obj["freq"] if "freq" in obj else obj["offset"]
          return Timestamp(obj["value"], tz=obj["tz"], freq=freq)
      elif typ == "nat":
          return NaT
      elif typ == "period":
          return Period(ordinal=obj["ordinal"], freq=obj["freq"])
      elif typ == "index":
          dtype = dtype_for(obj["dtype"])
          data = unconvert(obj["data"], dtype, obj.get("compress"))
          return Index(data, dtype=dtype, name=obj["name"])
      elif typ == "range_index":
          return RangeIndex(
              obj["start"], obj["stop"], obj["step"], name=obj["name"]
          )
      elif typ == "multi_index":
          dtype = dtype_for(obj["dtype"])
          data = unconvert(obj["data"], dtype, obj.get("compress"))
          data = [tuple(x) for x in data]
          return MultiIndex.from_tuples(data, names=obj["names"])
      elif typ == "period_index":
          data = unconvert(obj["data"], np.int64, obj.get("compress"))
          d = dict(name=obj["name"], freq=obj["freq"])
          freq = d.pop("freq", None)
          return PeriodIndex(PeriodArray(data, freq), **d)
  
      elif typ == "datetime_index":
          data = unconvert(obj["data"], np.int64, obj.get("compress"))
          d = dict(name=obj["name"], freq=obj["freq"])
          result = DatetimeIndex(data, **d)
          tz = obj["tz"]
  
          # reverse tz conversion
          if tz is not None:
              result = result.tz_localize("UTC").tz_convert(tz)
          return result
  
      elif typ in ("interval_index", "interval_array"):
          return globals()[obj["klass"]].from_arrays(
              obj["left"], obj["right"], obj["closed"], name=obj["name"]
          )
      elif typ == "category":
          from_codes = globals()[obj["klass"]].from_codes
          return from_codes(
              codes=obj["codes"],
              categories=obj["categories"],
              ordered=obj["ordered"],
          )
  
      elif typ == "interval":
          return Interval(obj["left"], obj["right"], obj["closed"])
      elif typ == "series":
          dtype = dtype_for(obj["dtype"])
          pd_dtype = pandas_dtype(dtype)
  
          index = obj["index"]
          result = Series(
              unconvert(obj["data"], dtype, obj["compress"]),
              index=index,
              dtype=pd_dtype,
              name=obj["name"],
          )
          return result
  
      elif typ == "block_manager":
          axes = obj["axes"]
  
          def create_block(b):
              values = _safe_reshape(
                  unconvert(
                      b["values"], dtype_for(b["dtype"]), b["compress"]
                  ),
                  b["shape"],
              )
  
              # locs handles duplicate column names, and should be used instead
              # of items; see GH 9618
              if "locs" in b:
                  placement = b["locs"]
              else:
                  placement = axes[0].get_indexer(b["items"])
  
              if is_datetime64tz_dtype(b["dtype"]):
                  assert isinstance(values, np.ndarray), type(values)
                  assert values.dtype == "M8[ns]", values.dtype
                  values = DatetimeArray(values, dtype=b["dtype"])
  
              return make_block(
                  values=values,
                  klass=getattr(internals, b["klass"]),
                  placement=placement,
                  dtype=b["dtype"],
              )
  
          blocks = [create_block(b) for b in obj["blocks"]]
          return globals()[obj["klass"]](BlockManager(blocks, axes))
      elif typ == "datetime":
          return parse(obj["data"])
      elif typ == "datetime64":
          return np.datetime64(parse(obj["data"]))
      elif typ == "date":
          return parse(obj["data"]).date()
      elif typ == "timedelta":
          return timedelta(*obj["data"])
      elif typ == "timedelta64":
          return np.timedelta64(int(obj["data"]))
      # elif typ == 'sparse_series':
      #    dtype = dtype_for(obj['dtype'])
      #    return SparseSeries(
      #        unconvert(obj['sp_values'], dtype, obj['compress']),
      #        sparse_index=obj['sp_index'], index=obj['index'],
      #        fill_value=obj['fill_value'], kind=obj['kind'], name=obj['name'])
      # elif typ == 'sparse_dataframe':
      #    return SparseDataFrame(
      #        obj['data'], columns=obj['columns'],
      #        default_fill_value=obj['default_fill_value'],
      #        default_kind=obj['default_kind']
      #    )
      # elif typ == 'sparse_panel':
      #    return SparsePanel(
      #        obj['data'], items=obj['items'],
      #        default_fill_value=obj['default_fill_value'],
      #        default_kind=obj['default_kind'])
      elif typ == "block_index":
          return globals()[obj["klass"]](
              obj["length"], obj["blocs"], obj["blengths"]
          )
      elif typ == "int_index":
          return globals()[obj["klass"]](obj["length"], obj["indices"])
      elif typ == "ndarray":
          return unconvert(
              obj["data"],
              np.typeDict[obj["dtype"]],
              obj.get("compress"),
          ).reshape(obj["shape"])
      elif typ == "np_scalar":
          if obj.get("sub_typ") == "np_complex":
              return c2f(obj["real"], obj["imag"], obj["dtype"])
          else:
              dtype = dtype_for(obj["dtype"])
              try:
                  return dtype(obj["data"])
              except (ValueError, TypeError):
                  return dtype.type(obj["data"])
      elif typ == "np_complex":
          return complex(obj["real"] + "+" + obj["imag"] + "j")
      elif isinstance(obj, (dict, list, set)):
          return obj
      else:
          return obj
  
imports: from datetime import date, datetime, timedelta
  import os
  from textwrap import dedent
  import warnings
  from dateutil.parser import parse
  import numpy
  import pandas.compat
  from pandas.compat import u, u_safe
  from pandas.errors import PerformanceWarning
  from pandas.util._move import BadMove, move_into_mutable_buffer
  from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64tz_dtype, is_object_dtype, needs_i8_conversion, pandas_dtype
  from pandas import Categorical, CategoricalIndex, DataFrame, DatetimeIndex, Float64Index, Index, Int64Index, Interval, IntervalIndex, MultiIndex, NaT, Panel, Period, PeriodIndex, RangeIndex, Series, TimedeltaIndex, Timestamp
  from pandas.core import internals
  from pandas.core.arrays import DatetimeArray, IntervalArray, PeriodArray
  from pandas.core.arrays.sparse import BlockIndex, IntIndex
  from pandas.core.generic import NDFrame
  from pandas.core.internals import BlockManager, _safe_reshape, make_block
  from pandas.core.sparse.api import SparseDataFrame, SparseSeries
  from pandas.io.common import _stringify_path, get_filepath_or_buffer
  from pandas.io.msgpack import ExtType, Packer, Unpacker
label_desc: Use image node_id 8 for calling a global function with example usage: decode(obj) and returns: obj, Timestamp, NaT, Period, Index, RangeIndex, MultiIndex, PeriodIndex, result, globals, from_codes, Interval, result, globals, make_block, parse, np, parse, timedelta, np, globals, globals, unconvert, c2f, complex, dtype, obj, obj, dtype