old_contents: #
  # Licensed to the Apache Software Foundation (ASF) under one or more
  # contributor license agreements.  See the NOTICE file distributed with
  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  import datetime
  import decimal
  from typing import Any, Tuple, TypeVar, Union, TYPE_CHECKING
  
  import numpy as np
  from pandas.api.extensions import ExtensionDtype
  
  if TYPE_CHECKING:
      from pyspark.pandas.base import IndexOpsMixin  # noqa: F401 (SPARK-37011)
      from pyspark.pandas.frame import DataFrame
      from pyspark.pandas.generic import Frame  # noqa: F401 (SPARK-37011)
      from pyspark.pandas.indexes.base import Index
      from pyspark.pandas.series import Series
  
  
  # TypeVars
  T = TypeVar("T")
  
  FrameLike = TypeVar("FrameLike", bound="Frame")
  IndexOpsLike = TypeVar("IndexOpsLike", bound="IndexOpsMixin")
  
  # Type aliases
  Scalar = Union[
      int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None
  ]
  
  # TODO: use the actual type parameters.
  Label = Tuple[Any, ...]
  Name = Union[Any, Label]
  
  Axis = Union[int, str]
  Dtype = Union[np.dtype, ExtensionDtype]
  
  DataFrameOrSeries = Union["DataFrame", "Series"]
  SeriesOrIndex = Union["Series", "Index"]
  
new_contents: #
  # Licensed to the Apache Software Foundation (ASF) under one or more
  # contributor license agreements.  See the NOTICE file distributed with
  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  import datetime
  import decimal
  from typing import Any, Tuple, TypeVar, Union, TYPE_CHECKING
  
  import numpy as np
  from pandas.api.extensions import ExtensionDtype
  
  if TYPE_CHECKING:
      from pyspark.pandas.base import IndexOpsMixin
      from pyspark.pandas.frame import DataFrame
      from pyspark.pandas.generic import Frame
      from pyspark.pandas.indexes.base import Index
      from pyspark.pandas.series import Series
  
  
  # TypeVars
  T = TypeVar("T")
  
  FrameLike = TypeVar("FrameLike", bound="Frame")
  IndexOpsLike = TypeVar("IndexOpsLike", bound="IndexOpsMixin")
  
  # Type aliases
  Scalar = Union[
      int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None
  ]
  
  # TODO: use the actual type parameters.
  Label = Tuple[Any, ...]
  Name = Union[Any, Label]
  
  Axis = Union[int, str]
  Dtype = Union[np.dtype, ExtensionDtype]
  
  DataFrameOrSeries = Union["DataFrame", "Series"]
  SeriesOrIndex = Union["Series", "Index"]
  
subject: Remove unnecessary 'noqa: F401' comments