code: def tile(cls, op: "DataFrameDateRange"):
      out = op.outputs[0]
      start = op.start
      end = op.end
      freq = op.freq
      periods = op.periods
      inclusive = op.inclusive
  
      chunk_length = (
          out.extra_params.raw_chunk_size or options.chunk_size
      )
      chunk_length = decide_chunk_sizes(
          out.shape, chunk_length, out.dtype.itemsize
      )[0]
  
      if inclusive in ("neither", "right"):
          # if left not close, add one more for the first chunk
          chunk_length = (chunk_length[0] + 1,) + chunk_length[1:]
  
      if freq is None:
          if periods > 1:
              freq = (end - op.start) / (periods - 1)
          else:
              freq = end - start
  
      out_chunks = []
      cum_nsplit = [0] + np.cumsum(chunk_length).tolist()
      for i, chunk_size in enumerate(chunk_length):
          chunk_op = op.copy().reset_key()
          chunk_op.periods = chunk_size
  
          if i > 0 or inclusive not in ("neither", "right"):
              # for chunks in the middle, all sides are inclusive
              chunk_op.inclusive = "both"
          elif 0 == i and inclusive == "neither":
              chunk_op.inclusive = "right"
  
          chunk_i_start = cum_nsplit[i]
          if chunk_i_start > 0:
              chunk_start = chunk_op.start = (
                  start + freq * chunk_i_start
              )
          else:
              chunk_start = chunk_op.start = start
          chunk_end = chunk_op.end = (
              chunk_start + (chunk_size - 1) * freq
          )
  
          # gen chunk index_value
          chunk_index_value = parse_index(
              out.index_value.to_pandas(), i, out
          )
          chunk_index_value.value._min_val = chunk_start
          chunk_index_value.value._min_val_close = True
          chunk_index_value.value._max_val = chunk_end
          chunk_index_value.value._max_val_close = True
          chunk_index_value.value._is_unique = True
          chunk_index_value.value._is_monotonic_increasing = True
  
          size = (
              chunk_size - 1
              if i == 0 and inclusive in ("neither", "right")
              else chunk_size
          )
          out_chunk = chunk_op.new_chunk(
              None,
              shape=(size,),
              index=(i,),
              dtype=out.dtype,
              index_value=chunk_index_value,
              name=out.name,
          )
          out_chunks.append(out_chunk)
  
      new_op = op.copy()
      params = out.params
      params["chunks"] = out_chunks
      params["nsplits"] = (tuple(c.shape[0] for c in out_chunks),)
      return new_op.new_indexes(None, kws=[params])
  
imports: import warnings
  from datetime import datetime, date, time
  import numpy
  import pandas
  from pandas import Timestamp, NaT
  from pandas.tseries.frequencies import to_offset
  from pandas.tseries.offsets import Tick
  from pandas._libs.tslibs import timezones
  from ...None import opcodes
  from ...config import options
  from ...core import OutputType
  from ...serialization.serializables import AnyField, Int64Field, BoolField, StringField
  from ...tensor.utils import decide_chunk_sizes
  from ...utils import pd_release_version, no_default
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index
label_desc: Use image node_id 3 for calling the DataFrameDateRange obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op