code: def pandas_dtype(dtype) -> DtypeObj:
      """
      Convert input into a pandas only dtype object or a numpy dtype object.
  
      Parameters
      ----------
      dtype : object to be converted
  
      Returns
      -------
      np.dtype or a pandas dtype
  
      Raises
      ------
      TypeError if not a dtype
  
      Examples
      --------
      >>> pd.api.types.pandas_dtype(int)
      dtype('int64')
      """
      # short-circuit
      if isinstance(dtype, np.ndarray):
          return dtype.dtype
      elif isinstance(dtype, (np.dtype, ExtensionDtype)):
          return dtype
  
      # registered extension types
      result = registry.find(dtype)
      if result is not None:
          if isinstance(result, type):
              # GH 31356, GH 54592
              warnings.warn(
                  f"Instantiating {result.__name__} without any arguments."
                  f"Pass a {result.__name__} instance to silence this warning.",
                  UserWarning,
                  stacklevel=find_stack_level(),
              )
              result = result()
          return result
  
      # try a numpy dtype
      # raise a consistent TypeError if failed
      try:
          with warnings.catch_warnings():
              # GH#51523 - Series.astype(np.integer) doesn't show
              # numpy deprecation warning of np.integer
              # Hence enabling DeprecationWarning
              warnings.simplefilter("always", DeprecationWarning)
              npdtype = np.dtype(dtype)
      except SyntaxError as err:
          # np.dtype uses `eval` which can raise SyntaxError
          raise TypeError(
              f"data type '{dtype}' not understood"
          ) from err
  
      # Any invalid dtype (such as pd.Timestamp) should raise an error.
      # np.dtype(invalid_type).kind = 0 for such objects. However, this will
      # also catch some valid dtypes such as object, np.object_ and 'object'
      # which we safeguard against by catching them earlier and returning
      # np.dtype(valid_dtype) before this condition is evaluated.
      if is_hashable(dtype) and dtype in [
          object,
          np.object_,
          "object",
          "O",
          "object_",
      ]:
          # check hashability to avoid errors/DeprecationWarning when we get
          # here and `dtype` is an array
          return npdtype
      elif npdtype.kind == "O":
          raise TypeError(f"dtype '{dtype}' not understood")
  
      return npdtype
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Any, Callable
  import warnings
  import numpy
  from pandas._libs import Interval, Period, algos, lib
  from pandas._libs.tslibs import conversion
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.base import _registry
  from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype, ExtensionDtype, IntervalDtype, PeriodDtype, SparseDtype
  from pandas.core.dtypes.generic import ABCIndex
  from pandas.core.dtypes.inference import is_array_like, is_bool, is_complex, is_dataclass, is_decimal, is_dict_like, is_file_like, is_float, is_hashable, is_integer, is_interval, is_iterator, is_list_like, is_named_tuple, is_nested_list_like, is_number, is_re, is_re_compilable, is_scalar, is_sequence
label_desc: Use image node_id 40 for calling a global function with example usage: pandas_dtype(dtype) and returns: npdtype, dtype, result, npdtype, dtype