parameters: (obj)
docstring: Return True if hash(obj) will succeed, False otherwise.
  
      Some types will pass a test against collections.abc.Hashable but fail when
      they are actually hashed with hash().
  
      Distinguish between these and other types by trying the call to hash() and
      seeing if they raise TypeError.
  
      Returns
      -------
      bool
  
      Examples
      --------
      >>> a = ([],)
      >>> isinstance(a, collections.abc.Hashable)
      True
      >>> is_hashable(a)
      False
docstring_summary: Return True if hash(obj) will succeed, False otherwise.
docstring_tokens: Return True if hash ( obj ) will succeed False otherwise .
function: def is_hashable(obj) -> bool:
      """
      Return True if hash(obj) will succeed, False otherwise.
  
      Some types will pass a test against collections.abc.Hashable but fail when
      they are actually hashed with hash().
  
      Distinguish between these and other types by trying the call to hash() and
      seeing if they raise TypeError.
  
      Returns
      -------
      bool
  
      Examples
      --------
      >>> a = ([],)
      >>> isinstance(a, collections.abc.Hashable)
      True
      >>> is_hashable(a)
      False
      """
      # Unfortunately, we can't use isinstance(obj, collections.abc.Hashable),
      # which can be faster than calling hash. That is because numpy scalars
      # fail this test.
  
      # Reconsider this decision once this numpy bug is fixed:
      # https://github.com/numpy/numpy/issues/5562
  
      try:
          hash(obj)
      except TypeError:
          return False
      else:
          return True
function_tokens: def is_hashable ( obj ) -> bool : # Unfortunately, we can't use isinstance(obj, collections.abc.Hashable), # which can be faster than calling hash. That is because numpy scalars # fail this test. # Reconsider this decision once this numpy bug is fixed: # https://github.com/numpy/numpy/issues/5562 try : hash ( obj ) except TypeError : return False else : return True