code: def _is_dtype(arr_or_dtype, condition):
      """
      Return a boolean if the condition is satisfied for the arr_or_dtype.
  
      Parameters
      ----------
      arr_or_dtype : array-like, str, np.dtype, or ExtensionArrayType
          The array-like or dtype object whose dtype we want to extract.
      condition : callable[Union[np.dtype, ExtensionDtype]]
  
      Returns
      -------
      bool
  
      """
  
      if arr_or_dtype is None:
          return False
      try:
          dtype = _get_dtype(arr_or_dtype)
      except (TypeError, ValueError, UnicodeEncodeError):
          return False
      return condition(dtype)
  
imports: import warnings
  import numpy
  from pandas._libs import algos, lib
  from pandas._libs.tslibs import conversion
  from pandas.compat import PY3, PY36, string_types
  from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype, ExtensionDtype, IntervalDtype, PandasExtensionDtype, PeriodDtype, registry
  from pandas.core.dtypes.generic import ABCCategorical, ABCDateOffset, ABCDatetimeIndex, ABCIndexClass, ABCPeriodArray, ABCPeriodIndex, ABCSeries
  from pandas.core.dtypes.inference import is_array_like, is_bool, is_complex, 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, is_string_like
label_desc: Use image node_id 46 for calling a global function with example usage: _is_dtype(arr_or_dtype, condition) and returns: condition, False, False