code: def prepare_spacing(
      spacing: int
      | float
      | np.ndarray
      | Sequence[int | float | np.ndarray | Sequence[int | float]]
      | None,
      batch_size: int,
      img_dim: int,
  ) -> Sequence[
      None | int | float | np.ndarray | Sequence[int | float]
  ]:
      """
      This function is used to prepare the `spacing` parameter to include batch dimension for the computation of
      surface distance, hausdorff distance or surface dice.
  
      An example with batch_size = 4 and img_dim = 3:
      input spacing = None -> output spacing = [None, None, None, None]
      input spacing = 0.8 -> output spacing = [0.8, 0.8, 0.8, 0.8]
      input spacing = [0.8, 0.5, 0.9] -> output spacing = [[0.8, 0.5, 0.9], [0.8, 0.5, 0.9], [0.8, 0.5, 0.9], [0.8, 0.5, 0.9]]
      input spacing = [0.8, 0.7, 1.2, 0.8] -> output spacing = [0.8, 0.7, 1.2, 0.8] (same as input)
  
      An example with batch_size = 3 and img_dim = 3:
      input spacing = [0.8, 0.5, 0.9] ->
      output spacing = [[0.8, 0.5, 0.9], [0.8, 0.5, 0.9], [0.8, 0.5, 0.9], [0.8, 0.5, 0.9]]
  
      Args:
          spacing: can be a float, a sequence of length `img_dim`, or a sequence with length `batch_size`
          that includes floats or sequences of length `img_dim`.
  
      Raises:
          ValueError: when `spacing` is a sequence of sequence, where the outer sequence length does not
          equal `batch_size` or inner sequence length does not equal `img_dim`.
  
      Returns:
          spacing: a sequence with length `batch_size` that includes integers, floats or sequences of length `img_dim`.
      """
      if spacing is None or isinstance(spacing, (int, float)):
          return list([spacing] * batch_size)
      if isinstance(spacing, (Sequence, np.ndarray)):
          if any(
              not isinstance(s, type(spacing[0])) for s in list(spacing)
          ):
              raise ValueError(
                  f"if `spacing` is a sequence, its elements should be of same type, got {spacing}."
              )
          if isinstance(spacing[0], (Sequence, np.ndarray)):
              if len(spacing) != batch_size:
                  raise ValueError(
                      "if `spacing` is a sequence of sequences, "
                      f"the outer sequence should have same length as batch size ({batch_size}), got {spacing}."
                  )
              if any(len(s) != img_dim for s in list(spacing)):
                  raise ValueError(
                      "each element of `spacing` list should either have same length as"
                      f"image dim ({img_dim}), got {spacing}."
                  )
              if not all(
                  isinstance(i, (int, float))
                  for s in list(spacing)
                  for i in list(s)
              ):
                  raise ValueError(
                      f"if `spacing` is a sequence of sequences or 2D np.ndarray, "
                      f"the elements should be integers or floats, got {spacing}."
                  )
              return list(spacing)
          if isinstance(spacing[0], (int, float)):
              if len(spacing) != img_dim:
                  raise ValueError(
                      f"if `spacing` is a sequence of numbers, "
                      f"it should have same length as image dim ({img_dim}), got {spacing}."
                  )
              return [spacing for _ in range(batch_size)]  # type: ignore
          raise ValueError(
              f"`spacing` is a sequence of elements with unsupported type: {type(spacing[0])}"
          )
      raise ValueError(
          f"`spacing` should either be a number, a sequence of numbers or a sequence of sequences, got {spacing}."
      )
  
imports: from __future__ import annotations
  import warnings
  from functools import lru_cache, partial
  from types import ModuleType
  from typing import Any, Iterable, Sequence
  import numpy
  import torch
  from monai.config import NdarrayOrTensor, NdarrayTensor
  from monai.transforms.croppad.dictionary import CropForegroundD
  from monai.transforms.utils import distance_transform_edt
  from monai.utils import MetricReduction, convert_to_cupy, convert_to_dst_type, convert_to_numpy, convert_to_tensor, deprecated_arg, deprecated_arg_default, ensure_tuple_rep, look_up_option, optional_import
label_desc: Use image node_id 8 for calling a global function with example usage: prepare_spacing(spacing, batch_size, img_dim) and returns: list, list, unknown