parameters: (a, b)
return_statement: return (needs_i8_conversion(a) and is_numeric(b)) or (
          needs_i8_conversion(b) and is_numeric(a)
      )
docstring: Check if we are comparing a datetime-like object to a numeric object.
      By "numeric," we mean an object that is either of an int or float dtype.
  
      Parameters
      ----------
      a : array-like, scalar
          The first object to check.
      b : array-like, scalar
          The second object to check.
  
      Returns
      -------
      boolean
          Whether we return a comparing a datetime-like to a numeric object.
  
      Examples
      --------
      >>> dt = np.datetime64(pd.datetime(2017, 1, 1))
      >>>
      >>> is_datetimelike_v_numeric(1, 1)
      False
      >>> is_datetimelike_v_numeric(dt, dt)
      False
      >>> is_datetimelike_v_numeric(1, dt)
      True
      >>> is_datetimelike_v_numeric(dt, 1)  # symmetric check
      True
      >>> is_datetimelike_v_numeric(np.array([dt]), 1)
      True
      >>> is_datetimelike_v_numeric(np.array([1]), dt)
      True
      >>> is_datetimelike_v_numeric(np.array([dt]), np.array([1]))
      True
      >>> is_datetimelike_v_numeric(np.array([1]), np.array([2]))
      False
      >>> is_datetimelike_v_numeric(np.array([dt]), np.array([dt]))
      False
docstring_summary: Check if we are comparing a datetime-like object to a numeric object.
      By "numeric," we mean an object that is either of an int or float dtype.
docstring_tokens: Check if we are comparing a datetime - like object to a numeric object . By numeric we mean an object that is either of an int or float dtype .
function: def is_datetimelike_v_numeric(a, b):
      """
      Check if we are comparing a datetime-like object to a numeric object.
      By "numeric," we mean an object that is either of an int or float dtype.
  
      Parameters
      ----------
      a : array-like, scalar
          The first object to check.
      b : array-like, scalar
          The second object to check.
  
      Returns
      -------
      boolean
          Whether we return a comparing a datetime-like to a numeric object.
  
      Examples
      --------
      >>> dt = np.datetime64(pd.datetime(2017, 1, 1))
      >>>
      >>> is_datetimelike_v_numeric(1, 1)
      False
      >>> is_datetimelike_v_numeric(dt, dt)
      False
      >>> is_datetimelike_v_numeric(1, dt)
      True
      >>> is_datetimelike_v_numeric(dt, 1)  # symmetric check
      True
      >>> is_datetimelike_v_numeric(np.array([dt]), 1)
      True
      >>> is_datetimelike_v_numeric(np.array([1]), dt)
      True
      >>> is_datetimelike_v_numeric(np.array([dt]), np.array([1]))
      True
      >>> is_datetimelike_v_numeric(np.array([1]), np.array([2]))
      False
      >>> is_datetimelike_v_numeric(np.array([dt]), np.array([dt]))
      False
      """
  
      if not hasattr(a, "dtype"):
          a = np.asarray(a)
      if not hasattr(b, "dtype"):
          b = np.asarray(b)
  
      def is_numeric(x):
          """
          Check if an object has a numeric dtype (i.e. integer or float).
          """
          return is_integer_dtype(x) or is_float_dtype(x)
  
      return (needs_i8_conversion(a) and is_numeric(b)) or (
          needs_i8_conversion(b) and is_numeric(a)
      )
function_tokens: def is_datetimelike_v_numeric ( a , b ) : if not hasattr ( a , "dtype" ) : a = np . asarray ( a ) if not hasattr ( b , "dtype" ) : b = np . asarray ( b ) def is_numeric ( x ) : """
          Check if an object has a numeric dtype (i.e. integer or float).
          """ return is_integer_dtype ( x ) or is_float_dtype ( x ) return ( needs_i8_conversion ( a ) and is_numeric ( b ) ) or ( needs_i8_conversion ( b ) and is_numeric ( a ) )