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 _binaryop(
      self, other: ColumnBinaryOperand, op: str
  ) -> ColumnBase:
      reflect, op = self._check_reflected_op(op)
      other = self._wrap_binop_normalization(other)
      if other is NotImplemented:
          return NotImplemented
  
      this: ColumnBinaryOperand = self
      out_dtype = None
  
      if is_timedelta64_dtype(other.dtype):
          # TODO: pandas will allow these operators to work but return false
          # when comparing to non-timedelta dtypes. We should do the same.
          if op in {
              "__eq__",
              "__ne__",
              "__lt__",
              "__gt__",
              "__le__",
              "__ge__",
              "NULL_EQUALS",
          }:
              out_dtype = cudf.dtype(np.bool_)
          elif op == "__mod__":
              out_dtype = determine_out_dtype(self.dtype, other.dtype)
          elif op in {"__truediv__", "__floordiv__"}:
              common_dtype = determine_out_dtype(
                  self.dtype, other.dtype
              )
              out_dtype = (
                  np.float64 if op == "__truediv__" else np.int64
              )
              this = self.astype(common_dtype).astype(out_dtype)
              if isinstance(other, cudf.Scalar):
                  if other.is_valid():
                      other = other.value.astype(common_dtype).astype(
                          out_dtype
                      )
                  else:
                      other = cudf.Scalar(None, out_dtype)
              else:
                  other = other.astype(common_dtype).astype(out_dtype)
          elif op in {"__add__", "__sub__"}:
              out_dtype = determine_out_dtype(self.dtype, other.dtype)
      elif other.dtype.kind in {"f", "i", "u"}:
          if op in {
              "__mul__",
              "__mod__",
              "__truediv__",
              "__floordiv__",
          }:
              out_dtype = self.dtype
          elif op in {"__eq__", "NULL_EQUALS", "__ne__"}:
              if isinstance(other, ColumnBase) and not isinstance(
                  other, TimeDeltaColumn
              ):
                  result = _all_bools_with_nulls(
                      self, other, bool_fill_value=op == "__ne__"
                  )
                  if cudf.get_option("mode.pandas_compatible"):
                      result = result.fillna(op == "__ne__")
                  return result
  
      if out_dtype is None:
          return NotImplemented
  
      lhs, rhs = (other, this) if reflect else (this, other)
  
      result = libcudf.binaryop.binaryop(lhs, rhs, op, out_dtype)
      if cudf.get_option(
          "mode.pandas_compatible"
      ) and out_dtype == cudf.dtype(np.bool_):
          result = result.fillna(op == "__ne__")
      return result
  
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 6 for calling the TimeDeltaColumn obj's underlying member method code with example usage: obj._binaryop(other, op) and returns: result, NotImplemented, NotImplemented, result