code: def convert_to_dst_type(
      src: Any,
      dst: NdarrayTensor,
      dtype: DtypeLike | torch.dtype | None = None,
      wrap_sequence: bool = False,
      device: None | str | torch.device = None,
      safe: bool = False,
  ) -> tuple[NdarrayTensor, type, torch.device | None]:
      """
      Convert source data to the same data type and device as the destination data.
      If `dst` is an instance of `torch.Tensor` or its subclass, convert `src` to `torch.Tensor` with the same data type as `dst`,
      if `dst` is an instance of `numpy.ndarray` or its subclass, convert to `numpy.ndarray` with the same data type as `dst`,
      otherwise, convert to the type of `dst` directly.
  
      Args:
          src: source data to convert type.
          dst: destination data that convert to the same data type as it.
          dtype: an optional argument if the target `dtype` is different from the original `dst`'s data type.
          wrap_sequence: if `False`, then lists will recursively call this function. E.g., `[1, 2]` -> `[array(1), array(2)]`.
              If `True`, then `[1, 2]` -> `array([1, 2])`.
          device: target device to put the converted Tensor data. If unspecified, `dst.device` will be used if possible.
          safe: if `True`, then do safe dtype convert when intensity overflow. default to `False`.
              E.g., `[256, -12]` -> `[array(0), array(244)]`. If `True`, then `[256, -12]` -> `[array(255), array(0)]`.
  
      See Also:
          :func:`convert_data_type`
      """
  
      device = (
          dst.device
          if device is None and isinstance(dst, torch.Tensor)
          else device
      )
      if dtype is None:
          dtype = getattr(dst, "dtype", None)  # sequence has no dtype
  
      copy_meta = False
      output_type: Any
      if isinstance(dst, monai.data.MetaTensor):
          output_type = monai.data.MetaTensor
          if not isinstance(src, monai.data.MetaTensor):
              copy_meta = True  # converting a non-meta tensor to a meta tensor, probably take the metadata as well.
      elif isinstance(dst, torch.Tensor):
          output_type = torch.Tensor
      elif isinstance(dst, np.ndarray):
          output_type = np.ndarray
      else:
          output_type = type(dst)
      output: NdarrayTensor
      output, _type, _device = convert_data_type(
          data=src,
          output_type=output_type,
          device=device,
          dtype=dtype,
          wrap_sequence=wrap_sequence,
          safe=safe,
      )
      if copy_meta and isinstance(output, monai.data.MetaTensor):
          output.copy_meta_from(dst)
      return output, _type, _device
  
imports: from __future__ import annotations
  import re
  from collections.abc import Sequence
  from typing import Any
  import numpy
  import torch
  import monai
  from monai.config.type_definitions import DtypeLike, NdarrayTensor
  from monai.utils import optional_import
label_desc: Use image node_id 11 for calling a global function with example usage: convert_to_dst_type(src, dst, dtype, wrap_sequence, device, safe) and returns: output,  _type,  _device