class_docstr: Parameters
  ----------
  data : Buffer
      The Timedelta values
  dtype : np.dtype
      The data type
  size : int
      Size of memory allocation.
  mask : Buffer; optional
      The validity mask
  offset : int
      Data offset
  null_count : int, optional
      The number of null values.
      If None, it is calculated automatically.
code: def to_pandas(
      self, *, index: Optional[pd.Index] = None, nullable: bool = False
  ) -> pd.Series:
      # `copy=True` workaround until following issue is fixed:
      # https://issues.apache.org/jira/browse/ARROW-9772
  
      # Pandas only supports `timedelta64[ns]` dtype
      # and conversion to this type is necessary to make
      # arrow to pandas conversion happen for large values.
      if nullable:
          raise NotImplementedError(f"{nullable=} is not implemented.")
  
      return pd.Series(
          self.astype("timedelta64[ns]").to_arrow(),
          copy=True,
          dtype=self.dtype,
          index=index,
      )
  
imports: from __future__ import annotations
  import datetime
  from typing import Any, Optional, Sequence, cast
  import numpy
  import pandas
  import pyarrow
  from typing_extensions import Self
  import cudf
  from cudf import _lib
  from cudf._typing import ColumnBinaryOperand, DatetimeLikeScalar, Dtype
  from cudf.api.types import is_scalar, is_timedelta64_dtype
  from cudf.core.buffer import Buffer, acquire_spill_lock
  from cudf.core.column import ColumnBase, column, string
  from cudf.utils.dtypes import np_to_pa_dtype
  from cudf.utils.utils import _all_bools_with_nulls
label_desc: Use image node_id 5 for calling the TimeDeltaColumn obj's underlying member method code with example usage: obj.to_pandas() and returns: pd