code: def take_along_axis(
      arr: Union[tf.Tensor, tf.Variable],
      indices: Union[tf.Tensor, tf.Variable],
      axis: int,
      /,
      *,
      mode: str = "fill",
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      if len(arr.shape) != len(indices.shape):
          raise ivy.utils.exceptions.IvyException(
              "arr and indices must have the same number of dimensions;"
              + f" got {len(arr.shape)} vs {len(indices.shape)}"
          )
      indices = tf.dtypes.cast(indices, tf.int32)
      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 += len(arr.shape)
      if mode == "clip":
          max_index = arr.shape[axis] - 1
          indices = tf.clip_by_value(indices, 0, max_index)
      elif mode in ("fill", "drop"):
          if "float" in str(arr.dtype) or "complex" in str(arr.dtype):
              fill_value = tf.constant(float("nan"), dtype=arr.dtype)
          elif "uint" in str(arr.dtype):
              fill_value = tf.constant(arr.dtype.max, dtype=arr.dtype)
          elif "int" in str(arr.dtype):
              fill_value = tf.constant(
                  -arr.dtype.max - 1, dtype=arr.dtype
              )
          else:
              raise TypeError(
                  f"Invalid dtype '{arr.dtype}'. Valid dtypes are 'float', 'complex',"
                  " 'uint', 'int'."
              )
          indices = tf.where(
              (indices < 0) | (indices >= arr.shape[axis]), -1, indices
          )
          arr_shape = list(arr_shape)
          arr_shape[axis] = 1
          fill_arr = tf.fill(arr_shape, fill_value)
          arr = tf.concat([arr, fill_arr], axis=axis)
      return tf.experimental.numpy.take_along_axis(arr, indices, axis)
  
imports: from collections import namedtuple
  from typing import Iterable, Union, Optional, Sequence, Tuple, NamedTuple, List, Literal, Callable, Any
  from numbers import Number
  import tensorflow
  from ivy.func_wrapper import with_unsupported_dtypes
  from ..None import backend_version
  import ivy
  from ivy.functional.ivy.experimental.manipulation import _to_tf_padding
label_desc: Use image node_id 16 for calling a global function with example usage: take_along_axis() and returns: tf