code: def merge(
      obj: DataFrame,
      right: DataFrame,
      how: str = "inner",
      on: Optional[Union[Name, List[Name]]] = None,
      left_on: Optional[Union[Name, List[Name]]] = None,
      right_on: Optional[Union[Name, List[Name]]] = None,
      left_index: bool = False,
      right_index: bool = False,
      suffixes: Tuple[str, str] = ("_x", "_y"),
  ) -> "DataFrame":
      """
      Merge DataFrame objects with a database-style join.
  
      The index of the resulting DataFrame will be one of the following:
          - 0...n if no index is used for merging
          - Index of the left DataFrame if merged only on the index of the right DataFrame
          - Index of the right DataFrame if merged only on the index of the left DataFrame
          - All involved indices if merged using the indices of both DataFrames
              e.g. if `left` with indices (a, x) and `right` with indices (b, x), the result will
              be an index (x, a, b)
  
      Parameters
      ----------
      right: Object to merge with.
      how: Type of merge to be performed.
          {'left', 'right', 'outer', 'inner'}, default 'inner'
  
          left: use only keys from left frame, like a SQL left outer join; preserve key
              order.
          right: use only keys from right frame, like a SQL right outer join; preserve key
              order.
          outer: use union of keys from both frames, like a SQL full outer join; sort keys
              lexicographically.
          inner: use intersection of keys from both frames, like a SQL inner join;
              preserve the order of the left keys.
      on: Column or index level names to join on. These must be found in both DataFrames. If on
          is None and not merging on indexes then this defaults to the intersection of the
          columns in both DataFrames.
      left_on: Column or index level names to join on in the left DataFrame. Can also
          be an array or list of arrays of the length of the left DataFrame.
          These arrays are treated as if they are columns.
      right_on: Column or index level names to join on in the right DataFrame. Can also
          be an array or list of arrays of the length of the right DataFrame.
          These arrays are treated as if they are columns.
      left_index: Use the index from the left DataFrame as the join key(s). If it is a
          MultiIndex, the number of keys in the other DataFrame (either the index or a number of
          columns) must match the number of levels.
      right_index: Use the index from the right DataFrame as the join key. Same caveats as
          left_index.
      suffixes: Suffix to apply to overlapping column names in the left and right side,
          respectively.
  
      Returns
      -------
      DataFrame
          A DataFrame of the two merged objects.
  
      Examples
      --------
  
      >>> df1 = ps.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
      ...                     'value': [1, 2, 3, 5]},
      ...                    columns=['lkey', 'value'])
      >>> df2 = ps.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
      ...                     'value': [5, 6, 7, 8]},
      ...                    columns=['rkey', 'value'])
      >>> df1
        lkey  value
      0  foo      1
      1  bar      2
      2  baz      3
      3  foo      5
      >>> df2
        rkey  value
      0  foo      5
      1  bar      6
      2  baz      7
      3  foo      8
  
      Merge df1 and df2 on the lkey and rkey columns. The value columns have
      the default suffixes, _x and _y, appended.
  
      >>> merged = ps.merge(df1, df2, left_on='lkey', right_on='rkey')
      >>> merged.sort_values(by=['lkey', 'value_x', 'rkey', 'value_y'])  # doctest: +ELLIPSIS
        lkey  value_x rkey  value_y
      ...bar        2  bar        6
      ...baz        3  baz        7
      ...foo        1  foo        5
      ...foo        1  foo        8
      ...foo        5  foo        5
      ...foo        5  foo        8
  
      >>> left_psdf = ps.DataFrame({'A': [1, 2]})
      >>> right_psdf = ps.DataFrame({'B': ['x', 'y']}, index=[1, 2])
  
      >>> ps.merge(left_psdf, right_psdf, left_index=True, right_index=True).sort_index()
         A  B
      1  2  x
  
      >>> ps.merge(left_psdf, right_psdf, left_index=True, right_index=True, how='left').sort_index()
         A     B
      0  1  None
      1  2     x
  
      >>> ps.merge(left_psdf, right_psdf, left_index=True, right_index=True, how='right').sort_index()
           A  B
      1  2.0  x
      2  NaN  y
  
      >>> ps.merge(left_psdf, right_psdf, left_index=True, right_index=True, how='outer').sort_index()
           A     B
      0  1.0  None
      1  2.0     x
      2  NaN     y
  
      Notes
      -----
      As described in #263, joining string columns currently returns None for missing values
          instead of NaN.
      """
      return obj.merge(
          right,
          how=how,
          on=on,
          left_on=left_on,
          right_on=right_on,
          left_index=left_index,
          right_index=right_index,
          suffixes=suffixes,
      )
  
imports: from typing import Any, Callable, Dict, List, Optional, Set, Sized, Tuple, Type, Union, cast, no_type_check
  from collections.abc import Iterable
  from datetime import tzinfo
  from functools import reduce
  from io import BytesIO
  import json
  import warnings
  import numpy
  import pandas
  from pandas.api.types import is_datetime64_dtype, is_list_like
  from pandas.tseries.offsets import DateOffset
  import pyarrow
  import pyarrow.parquet
  from pyspark.sql import functions, Column
  from pyspark.sql.functions import pandas_udf
  from pyspark.sql.types import ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, BooleanType, TimestampType, TimestampNTZType, DecimalType, StringType, DateType, StructType, DataType
  from pyspark.sql.dataframe import DataFrame
  from pyspark import pandas
  from pyspark.pandas._typing import Axis, Dtype, Label, Name
  from pyspark.pandas.base import IndexOpsMixin
  from pyspark.pandas.utils import align_diff_frames, default_session, is_name_like_tuple, is_name_like_value, name_like_string, same_anchor, scol_for, validate_axis, log_advice
  from pyspark.pandas.frame import DataFrame, _reduce_spark_multi
  from pyspark.pandas.internal import InternalFrame, DEFAULT_SERIES_NAME, HIDDEN_COLUMNS, SPARK_INDEX_NAME_FORMAT
  from pyspark.pandas.series import Series, first_series
  from pyspark.pandas.spark.utils import as_nullable_spark_type, force_decimal_precision_scale
  from pyspark.pandas.indexes import Index, DatetimeIndex, TimedeltaIndex
  from pyspark.pandas.indexes.multi import MultiIndex
  from pyspark.sql.utils import get_column_class
label_desc: Use image node_id 24 for calling a global function with example usage: merge(obj, right, how, on, left_on, right_on, left_index, right_index, suffixes) and returns: obj