code: def take_1d(
      arr: ArrayLike,
      indexer: npt.NDArray[np.intp],
      fill_value=None,
      allow_fill: bool = True,
      mask: npt.NDArray[np.bool_] | None = None,
  ) -> ArrayLike:
      """
      Specialized version for 1D arrays. Differences compared to `take_nd`:
  
      - Assumes input array has already been converted to numpy array / EA
      - Assumes indexer is already guaranteed to be intp dtype ndarray
      - Only works for 1D arrays
  
      To ensure the lowest possible overhead.
  
      Note: similarly to `take_nd`, this function assumes that the indexer is
      a valid(ated) indexer with no out of bound indices.
  
      Parameters
      ----------
      arr : np.ndarray or ExtensionArray
          Input array.
      indexer : ndarray
          1-D array of indices to take (validated indices, intp dtype).
      fill_value : any, default np.nan
          Fill value to replace -1 values with
      allow_fill : bool, default True
          If False, indexer is assumed to contain no -1 values so no filling
          will be done.  This short-circuits computation of a mask. Result is
          undefined if allow_fill == False and -1 is present in indexer.
      mask : np.ndarray, optional, default None
          If `allow_fill` is True, and the mask (where indexer == -1) is already
          known, it can be passed to avoid recomputation.
      """
      if not isinstance(arr, np.ndarray):
          # ExtensionArray -> dispatch to their method
          return arr.take(
              indexer, fill_value=fill_value, allow_fill=allow_fill
          )
  
      if not allow_fill:
          return arr.take(indexer)
  
      (
          dtype,
          fill_value,
          mask_info,
      ) = _take_preprocess_indexer_and_fill_value(
          arr, indexer, fill_value, True, mask
      )
  
      # at this point, it's guaranteed that dtype can hold both the arr values
      # and the fill_value
      out = np.empty(indexer.shape, dtype=dtype)
  
      func = _get_take_nd_function(
          arr.ndim, arr.dtype, out.dtype, axis=0, mask_info=mask_info
      )
      func(arr, indexer, out, fill_value)
  
      return out
  
imports: from __future__ import annotations
  import functools
  from typing import TYPE_CHECKING, cast, overload
  import numpy
  from pandas._libs import algos, lib
  from pandas.core.dtypes.cast import maybe_promote
  from pandas.core.dtypes.common import ensure_platform_int, is_1d_only_ea_dtype
  from pandas.core.dtypes.missing import na_value_for_dtype
  from pandas.core.construction import ensure_wrapped_if_datetimelike
label_desc: Use image node_id 5 for calling a global function with example usage: take_1d(arr, indexer, fill_value, allow_fill, mask) and returns: out, arr, arr