code: def pandas_dtype(dtype):
      
      # short-circuit
      if isinstance(dtype, np.ndarray):
          return dtype.dtype
      elif isinstance(dtype, (np.dtype, PandasExtensionDtype, ExtensionDtype)):
          return dtype
  
      # registered extension types
      result = registry.find(dtype)
      if result is not None:
          return result
  
      # try a numpy dtype
      # raise a consistent TypeError if failed
      try:
          npdtype = np.dtype(dtype)
      except Exception:
          # we don't want to force a repr of the non-string
          if not isinstance(dtype, str):
              raise TypeError("data type not understood")
          raise TypeError("data type '{}' not understood".format(
              dtype))
  
      # 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']:
          # check hashability to avoid errors/DeprecationWarning when we get
          # here and `dtype` is an array
          return npdtype
      elif npdtype.kind == 'O':
          raise TypeError("dtype '{}' not understood".format(dtype))
  
      return npdtype
docs: 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