code: def take_along_axis(
      arr: torch.Tensor,
      indices: torch.Tensor,
      axis: int,
      /,
      *,
      mode: str = "fill",
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      if arr.ndim != indices.ndim:
          raise ivy.utils.exceptions.IvyException(
              "arr and indices must have the same number of dimensions;"
              + f" got {arr.ndim} vs {indices.ndim}"
          )
      indices = indices.long()
      if mode not in ["clip", "fill", "drop"]:
          raise ValueError(
              f"Invalid mode '{mode}'. Valid modes are 'clip', 'fill', 'drop'."
          )
      arr_shape = arr.shape
      if axis < 0:
          axis += arr.ndim
      if mode == "clip":
          max_index = arr.shape[axis] - 1
          indices = torch.clamp(indices, 0, max_index)
      elif mode in {"fill", "drop"}:
          if "float" in str(arr.dtype) or "complex" in str(arr.dtype):
              fill_value = float("nan")
          elif "uint" in str(arr.dtype):
              fill_value = torch.iinfo(arr.dtype).max
          elif "int" in str(arr.dtype):
              fill_value = -torch.iinfo(arr.dtype).max - 1
          indices = torch.where(
              (indices < 0) | (indices >= arr.shape[axis]), -1, indices
          )
          arr_shape = list(arr_shape)
          arr_shape[axis] = 1
          fill_arr = torch.full(arr_shape, fill_value, dtype=arr.dtype)
          arr = torch.cat([arr, fill_arr], dim=axis)
          indices = torch.where(
              indices < 0, arr.shape[axis] + indices, indices
          )
      return torch.take_along_dim(arr, indices, axis, out=out)
  
imports: from typing import Iterable, Optional, Union, Sequence, Tuple, NamedTuple, List, Literal, Callable, Any
  from numbers import Number
  from collections import namedtuple
  import torch
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from ..None import backend_version
  import ivy
  from ivy.functional.ivy.experimental.manipulation import _to_tf_padding, _check_paddle_pad, _to_paddle_padding
label_desc: Use image node_id 19 for calling a global function with example usage: take_along_axis() and returns: torch