code: def meshgrid(
      *arrays: paddle.Tensor,
      sparse: bool = False,
      indexing: str = "xy",
      out: Optional[paddle.Tensor] = None,
  ) -> List[paddle.Tensor]:
      if len(arrays) == 1:
          return arrays
      if not sparse:
          if indexing == "ij":
              return paddle.meshgrid(*arrays)
          elif indexing == "xy":
  
              def index_switch(x):
                  return (
                      paddle_backend.swapaxes(x, 0, 1)
                      if x.ndim > 1
                      else x
                  )
  
              arrays = list(map(index_switch, arrays))
              ret = paddle.meshgrid(*arrays)
              return list(map(index_switch, ret))
          else:
              raise ValueError(
                  f"indexing must be either 'ij' or 'xy', got {indexing}"
              )
  
      sd = (1,) * len(arrays)
      res = [
          paddle.reshape(
              paddle.to_tensor(a), (sd[:i] + (-1,) + sd[i + 1 :])
          )
          for i, a in enumerate(arrays)
      ]
      if indexing == "xy" and len(arrays) > 1:
          res[0] = paddle.reshape(res[0], (1, -1) + sd[2:])
          res[1] = paddle.reshape(res[1], (-1, 1) + sd[2:])
  
      return res
  
imports: import struct
  from numbers import Number
  from typing import Union, List, Optional, Sequence, Tuple
  import numpy
  import paddle
  import ivy.functional.backends.paddle
  import ivy
  from ivy.func_wrapper import with_unsupported_device_and_dtypes
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_handle_nestable, _asarray_infer_dtype, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes, _remove_np_bfloat16
  from .None import backend_version
  from paddle.device import core
label_desc: Use image node_id 14 for calling a global function with example usage: meshgrid() and returns: res, arrays, paddle, list, unknown