code: def concat(
      objs: Iterable[Series | DataFrame]
      | Mapping[HashableT, Series | DataFrame],
      *,
      axis: Axis = 0,
      join: str = "outer",
      ignore_index: bool = False,
      keys: Iterable[Hashable] | None = None,
      levels=None,
      names: list[HashableT] | None = None,
      verify_integrity: bool = False,
      sort: bool = False,
      copy: bool | None = None,
  ) -> DataFrame | Series:
      """
      Concatenate pandas objects along a particular axis.
  
      Allows optional set logic along the other axes.
  
      Can also add a layer of hierarchical indexing on the concatenation axis,
      which may be useful if the labels are the same (or overlapping) on
      the passed axis number.
  
      Parameters
      ----------
      objs : an iterable or mapping of Series or DataFrame objects
          If a mapping is passed, the sorted keys will be used as the `keys`
          argument, unless it is passed, in which case the values will be
          selected (see below). Any None objects will be dropped silently unless
          they are all None in which case a ValueError will be raised.
      axis : {0/'index', 1/'columns'}, default 0
          The axis to concatenate along.
      join : {'inner', 'outer'}, default 'outer'
          How to handle indexes on other axis (or axes).
      ignore_index : bool, default False
          If True, do not use the index values along the concatenation axis. The
          resulting axis will be labeled 0, ..., n - 1. This is useful if you are
          concatenating objects where the concatenation axis does not have
          meaningful indexing information. Note the index values on the other
          axes are still respected in the join.
      keys : sequence, default None
          If multiple levels passed, should contain tuples. Construct
          hierarchical index using the passed keys as the outermost level.
      levels : list of sequences, default None
          Specific levels (unique values) to use for constructing a
          MultiIndex. Otherwise they will be inferred from the keys.
      names : list, default None
          Names for the levels in the resulting hierarchical index.
      verify_integrity : bool, default False
          Check whether the new concatenated axis contains duplicates. This can
          be very expensive relative to the actual data concatenation.
      sort : bool, default False
          Sort non-concatenation axis if it is not already aligned.
  
      copy : bool, default True
          If False, do not copy data unnecessarily.
  
      Returns
      -------
      object, type of objs
          When concatenating all ``Series`` along the index (axis=0), a
          ``Series`` is returned. When ``objs`` contains at least one
          ``DataFrame``, a ``DataFrame`` is returned. When concatenating along
          the columns (axis=1), a ``DataFrame`` is returned.
  
      See Also
      --------
      DataFrame.join : Join DataFrames using indexes.
      DataFrame.merge : Merge DataFrames by indexes or columns.
  
      Notes
      -----
      The keys, levels, and names arguments are all optional.
  
      A walkthrough of how this method fits in with other tools for combining
      pandas objects can be found `here
      <https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html>`__.
  
      It is not recommended to build DataFrames by adding single rows in a
      for loop. Build a list of rows and make a DataFrame in a single concat.
  
      Examples
      --------
      Combine two ``Series``.
  
      >>> s1 = pd.Series(['a', 'b'])
      >>> s2 = pd.Series(['c', 'd'])
      >>> pd.concat([s1, s2])
      0    a
      1    b
      0    c
      1    d
      dtype: object
  
      Clear the existing index and reset it in the result
      by setting the ``ignore_index`` option to ``True``.
  
      >>> pd.concat([s1, s2], ignore_index=True)
      0    a
      1    b
      2    c
      3    d
      dtype: object
  
      Add a hierarchical index at the outermost level of
      the data with the ``keys`` option.
  
      >>> pd.concat([s1, s2], keys=['s1', 's2'])
      s1  0    a
          1    b
      s2  0    c
          1    d
      dtype: object
  
      Label the index keys you create with the ``names`` option.
  
      >>> pd.concat([s1, s2], keys=['s1', 's2'],
      ...           names=['Series name', 'Row ID'])
      Series name  Row ID
      s1           0         a
                   1         b
      s2           0         c
                   1         d
      dtype: object
  
      Combine two ``DataFrame`` objects with identical columns.
  
      >>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
      ...                    columns=['letter', 'number'])
      >>> df1
        letter  number
      0      a       1
      1      b       2
      >>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
      ...                    columns=['letter', 'number'])
      >>> df2
        letter  number
      0      c       3
      1      d       4
      >>> pd.concat([df1, df2])
        letter  number
      0      a       1
      1      b       2
      0      c       3
      1      d       4
  
      Combine ``DataFrame`` objects with overlapping columns
      and return everything. Columns outside the intersection will
      be filled with ``NaN`` values.
  
      >>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
      ...                    columns=['letter', 'number', 'animal'])
      >>> df3
        letter  number animal
      0      c       3    cat
      1      d       4    dog
      >>> pd.concat([df1, df3], sort=False)
        letter  number animal
      0      a       1    NaN
      1      b       2    NaN
      0      c       3    cat
      1      d       4    dog
  
      Combine ``DataFrame`` objects with overlapping columns
      and return only those that are shared by passing ``inner`` to
      the ``join`` keyword argument.
  
      >>> pd.concat([df1, df3], join="inner")
        letter  number
      0      a       1
      1      b       2
      0      c       3
      1      d       4
  
      Combine ``DataFrame`` objects horizontally along the x axis by
      passing in ``axis=1``.
  
      >>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],
      ...                    columns=['animal', 'name'])
      >>> pd.concat([df1, df4], axis=1)
        letter  number  animal    name
      0      a       1    bird   polly
      1      b       2  monkey  george
  
      Prevent the result from including duplicate index values with the
      ``verify_integrity`` option.
  
      >>> df5 = pd.DataFrame([1], index=['a'])
      >>> df5
         0
      a  1
      >>> df6 = pd.DataFrame([2], index=['a'])
      >>> df6
         0
      a  2
      >>> pd.concat([df5, df6], verify_integrity=True)
      Traceback (most recent call last):
          ...
      ValueError: Indexes have overlapping values: ['a']
  
      Append a single row to the end of a ``DataFrame`` object.
  
      >>> df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])
      >>> df7
          a   b
      0   1   2
      >>> new_row = pd.Series({'a': 3, 'b': 4})
      >>> new_row
      a    3
      b    4
      dtype: int64
      >>> pd.concat([df7, new_row.to_frame().T], ignore_index=True)
          a   b
      0   1   2
      1   3   4
      """
      if copy is None:
          if using_copy_on_write():
              copy = False
          else:
              copy = True
      elif copy and using_copy_on_write():
          copy = False
  
      op = _Concatenator(
          objs,
          axis=axis,
          ignore_index=ignore_index,
          join=join,
          keys=keys,
          levels=levels,
          names=names,
          verify_integrity=verify_integrity,
          copy=copy,
          sort=sort,
      )
  
      return op.get_result()
  
imports: from __future__ import annotations
  from collections import abc
  from typing import TYPE_CHECKING, Callable, Literal, cast, overload
  import warnings
  import numpy
  from pandas._config import using_copy_on_write
  from pandas.util._decorators import cache_readonly
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.common import is_bool, is_iterator
  from pandas.core.dtypes.concat import concat_compat
  from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
  from pandas.core.dtypes.missing import isna
  from pandas.core.arrays.categorical import factorize_from_iterable, factorize_from_iterables
  import pandas.core.common
  from pandas.core.indexes.api import Index, MultiIndex, all_indexes_same, default_index, ensure_index, get_objs_combined_axis, get_unanimous_names
  from pandas.core.internals import concatenate_managers
label_desc: Use image node_id 6 for calling a global function with example usage: concat(objs) and returns: op