code: def result_type(
      *arrays_and_dtypes: Union[torch.tensor, torch.dtype]
  ) -> ivy.Dtype:
      input = []
      for val in arrays_and_dtypes:
          torch_val = as_native_dtype(val)
          if isinstance(torch_val, torch.dtype):
              torch_val = torch.tensor(1, dtype=torch_val)
          input.append(torch_val)
  
      result = torch.tensor(
          1, dtype=torch.result_type(input[0], input[1])
      )
  
      for i in range(2, len(input)):
          result = torch.tensor(
              1, dtype=torch.result_type(result, input[i])
          )
      return as_ivy_dtype(result.dtype)
  
imports: from typing import Optional, Union, Sequence, List
  import numpy
  import torch
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.ivy.data_type import _handle_nestable_dtype_info
  from .None import backend_version
label_desc: Use image node_id 6 for calling a global function with example usage: result_type() and returns: as_ivy_dtype