code: def meshgrid(
      *arrays: torch.Tensor,
      sparse: bool = False,
      indexing: str = "xy",
      out: Optional[torch.Tensor] = None,
  ) -> List[torch.Tensor]:
      if not sparse:
          return list(torch.meshgrid(*arrays, indexing=indexing))
  
      sd = (1,) * len(arrays)
      res = [
          torch.reshape(
              torch.as_tensor(a), (sd[:i] + (-1,) + sd[i + 1 :])
          )
          for i, a in enumerate(arrays)
      ]
  
      if indexing == "xy" and len(arrays) > 1:
          res[0] = torch.reshape(res[0], (1, -1) + sd[2:])
          res[1] = torch.reshape(res[1], (-1, 1) + sd[2:])
  
      return res
  
imports: import copy
  from numbers import Number
  from typing import Union, List, Optional, Sequence, Tuple
  import numpy
  import torch
  from torch import Tensor
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_unsupported_device_and_dtypes
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_infer_dtype, _asarray_handle_nestable, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes, _remove_np_bfloat16
  from .None import backend_version
label_desc: Use image node_id 15 for calling a global function with example usage: meshgrid() and returns: res, list