code: def _asarray_infer_dtype(fn: Callable) -> Callable:
      @functools.wraps(fn)
      def _asarray_infer_dtype_wrapper(*args, dtype=None, **kwargs):
          """Determine the correct `dtype`, and then calls the function with the
          `dtype` passed explicitly. This wrapper is specifically for the backend
          implementations of asarray.
  
          Parameters
          ----------
          args
              The arguments to be passed to the function.
  
          dtype
              The dtype for the function.
  
          kwargs
              The keyword arguments to be passed to the function.
  
          Returns
          -------
              The return of the function, with `dtype` passed explicitly.
          """
  
          def _infer_dtype(obj):
              if isinstance(obj, ivy.NativeShape):
                  obj = list(obj)
              if hasattr(obj, "dtype"):
                  return (
                      obj.dtype.name
                      if isinstance(obj, np.ndarray)
                      else obj.dtype
                  )
              else:
                  return ivy.default_dtype(item=obj)
  
          if not ivy.exists(dtype):
              arr = args[0]
              # get default dtypes for all elements
              dtype_list = [
                  ivy.nested_map(
                      lambda x: _infer_dtype(x), arr, shallow=False
                  )
              ]
              # flatten the nested structure
              dtype_list = _flatten_nest(dtype_list)
              # keep unique dtypes
              dtype_list = list(set(dtype_list))
              if len(dtype_list) != 0:  # handle the case of empty input
                  # promote all dtypes to a single dtype
                  dtype = dtype_list[0]
                  # we disable precise mode to avoid wider than necessary casting
                  # that might result from the mixing of int32 and float32
                  with ivy.PreciseMode(False):
                      for dt in dtype_list[1:]:
                          dtype = ivy.promote_types(dtype, dt)
              else:
                  dtype = ivy.default_float_dtype()
              dtype = ivy.as_native_dtype(dtype)
          # call the function with dtype provided explicitly
          return fn(*args, dtype=dtype, **kwargs)
  
      _asarray_infer_dtype_wrapper.infer_dtype = True
      return _asarray_infer_dtype_wrapper
  
imports: from __future__ import annotations
  import functools
  from numbers import Number
  from typing import Union, Tuple, Optional, List, Sequence, Callable, Protocol, TypeVar, Iterable
  import numpy
  import ivy
  from ivy import to_ivy
  from ivy.utils.exceptions import handle_exceptions
  from ivy.utils.backend import current_backend
  from ivy.func_wrapper import handle_array_function, infer_dtype, handle_out_argument, outputs_to_ivy_arrays, inputs_to_native_arrays, inputs_to_native_shapes, to_native_arrays_and_back, handle_nestable, handle_array_like_without_promotion, handle_device, handle_backend_invalid, temp_asarray_wrapper
label_desc: Use image node_id 7 for calling a global function with example usage: _asarray_infer_dtype(fn) and returns: _asarray_infer_dtype_wrapper, fn, unknown, ivy