code: def concat_pandas(
      dfs,
      axis=0,
      join="outer",
      uniform=False,
      filter_warning=True,
      ignore_index=False,
      **kwargs,
  ):
      ignore_order = kwargs.pop("ignore_order", False)
  
      if axis == 1:
          return pd.concat(dfs, axis=axis, join=join, **kwargs)
  
      # Support concatenating indices along axis 0
      if isinstance(dfs[0], pd.Index):
          if isinstance(dfs[0], pd.CategoricalIndex):
              for i in range(1, len(dfs)):
                  if not isinstance(dfs[i], pd.CategoricalIndex):
                      dfs[i] = dfs[i].astype("category")
              return pd.CategoricalIndex(
                  union_categoricals(dfs, ignore_order=ignore_order),
                  name=dfs[0].name,
              )
          elif isinstance(dfs[0], pd.MultiIndex):
              first, rest = dfs[0], dfs[1:]
              if all(
                  (
                      isinstance(o, pd.MultiIndex)
                      and o.nlevels >= first.nlevels
                  )
                  for o in rest
              ):
                  arrays = [
                      concat([i._get_level_values(n) for i in dfs])
                      for n in range(first.nlevels)
                  ]
                  return pd.MultiIndex.from_arrays(
                      arrays, names=first.names
                  )
  
              to_concat = (first.values,) + tuple(
                  k._values for k in rest
              )
              new_tuples = np.concatenate(to_concat)
              try:
                  return pd.MultiIndex.from_tuples(
                      new_tuples, names=first.names
                  )
              except Exception:
                  return pd.Index(new_tuples)
          return dfs[0].append(dfs[1:])
  
      # Handle categorical index separately
      dfs0_index = dfs[0].index
  
      has_categoricalindex = isinstance(
          dfs0_index, pd.CategoricalIndex
      ) or (
          isinstance(dfs0_index, pd.MultiIndex)
          and any(
              isinstance(i, pd.CategoricalIndex)
              for i in dfs0_index.levels
          )
      )
  
      if has_categoricalindex:
          dfs2 = [df.reset_index(drop=True) for df in dfs]
          ind = concat([df.index for df in dfs])
      else:
          dfs2 = dfs
          ind = None
  
      # Concatenate the partitions together, handling categories as needed
      if (
          isinstance(dfs2[0], pd.DataFrame)
          if uniform
          else any(isinstance(df, pd.DataFrame) for df in dfs2)
      ):
          if uniform:
              dfs3 = dfs2
              cat_mask = dfs2[0].dtypes == "category"
          else:
              # When concatenating mixed dataframes and series on axis 1, Pandas
              # converts series to dataframes with a single column named 0, then
              # concatenates.
              dfs3 = [
                  df
                  if isinstance(df, pd.DataFrame)
                  else df.to_frame().rename(columns={df.name: 0})
                  for df in dfs2
              ]
              # pandas may raise a RuntimeWarning for comparing ints and strs
              with warnings.catch_warnings():
                  warnings.simplefilter("ignore", RuntimeWarning)
                  if filter_warning:
                      warnings.simplefilter("ignore", FutureWarning)
                  cat_mask = pd.concat(
                      [
                          (df.dtypes == "category").to_frame().T
                          for df in dfs3
                      ],
                      join=join,
                      **kwargs,
                  ).any()
  
          if cat_mask.any():
              not_cat = cat_mask[~cat_mask].index
              # this should be aligned, so no need to filter warning
              out = pd.concat(
                  [df[df.columns.intersection(not_cat)] for df in dfs3],
                  join=join,
                  **kwargs,
              )
              temp_ind = out.index
              for col in cat_mask.index.difference(not_cat):
                  # Find an example of categoricals in this column
                  for df in dfs3:
                      sample = df.get(col)
                      if sample is not None:
                          break
                  # Extract partitions, subbing in missing if needed
                  parts = []
                  for df in dfs3:
                      if col in df.columns:
                          parts.append(df[col])
                      else:
                          codes = np.full(len(df), -1, dtype="i8")
                          data = pd.Categorical.from_codes(
                              codes,
                              sample.cat.categories,
                              sample.cat.ordered,
                          )
                          parts.append(data)
                  out[col] = union_categoricals(
                      parts, ignore_order=ignore_order
                  )
                  # Pandas resets index type on assignment if frame is empty
                  # https://github.com/pandas-dev/pandas/issues/17101
                  if not len(temp_ind):
                      out.index = temp_ind
              out = out.reindex(columns=cat_mask.index)
          else:
              # pandas may raise a RuntimeWarning for comparing ints and strs
              with warnings.catch_warnings():
                  warnings.simplefilter("ignore", RuntimeWarning)
                  if filter_warning:
                      warnings.simplefilter("ignore", FutureWarning)
                  out = pd.concat(dfs3, join=join, sort=False)
      else:
          if isinstance(dfs2[0].dtype, pd.CategoricalDtype):
              if ind is None:
                  ind = concat([df.index for df in dfs2])
              return pd.Series(
                  union_categoricals(dfs2, ignore_order=ignore_order),
                  index=ind,
                  name=dfs2[0].name,
              )
          with warnings.catch_warnings():
              if filter_warning:
                  warnings.simplefilter("ignore", FutureWarning)
  
              out = pd.concat(dfs2, join=join, **kwargs)
      # Re-add the index if needed
      if ind is not None:
          out.index = ind
      return out
  
imports: from __future__ import annotations
  import warnings
  from collections.abc import Iterable
  import numpy
  import pandas
  from pandas.api.types import is_scalar, union_categoricals
  from dask.array.core import Array
  from dask.array.dispatch import percentile_lookup
  from dask.array.percentile import _percentile
  from dask.backends import CreationDispatch, DaskBackendEntrypoint
  from dask.dataframe._compat import is_any_real_numeric_dtype
  from dask.dataframe.core import DataFrame, Index, Scalar, Series, _Frame
  from dask.dataframe.dispatch import categorical_dtype_dispatch, concat, concat_dispatch, from_pyarrow_table_dispatch, get_parallel_type, group_split_dispatch, grouper_dispatch, hash_object_dispatch, is_categorical_dtype_dispatch, make_meta_dispatch, make_meta_obj, meta_lib_from_array, meta_nonempty, partd_encode_dispatch, pyarrow_schema_dispatch, to_pandas_dispatch, to_pyarrow_table_dispatch, tolist_dispatch, union_categoricals_dispatch
  from dask.dataframe.extensions import make_array_nonempty, make_scalar
  from dask.dataframe.utils import _empty_series, _nonempty_scalar, _scalar_from_dtype, is_float_na_dtype, is_integer_na_dtype
  from dask.sizeof import SimpleSizeof, sizeof
  from dask.utils import is_arraylike, is_series_like, typename
label_desc: Use image node_id 25 for calling a global function with example usage: concat_pandas(dfs, axis, join, uniform, filter_warning, ignore_index) and returns: out, pd, dfs, pd, pd, pd, pd, pd