class_docstr: Pair for Python number (:class:`int`, :class:`float`, and :class:`complex`) inputs.
  
  .. note::
  
      If ``numpy`` is available, also handles :class:`numpy.number` inputs.
  
  Kwargs:
      rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
          values based on the type are selected with the below table.
      atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
          values based on the type are selected with the below table.
      equal_nan (bool): If ``True``, two ``NaN`` values are considered equal. Defaults to ``False``.
      check_dtype (bool): If ``True``, the type of the inputs will be checked for equality. Defaults to ``False``.
  
  The following table displays correspondence between Python number type and the ``torch.dtype``'s. See
  :func:`assert_close` for the corresponding tolerances.
  
  +------------------+-------------------------------+
  | ``type``         | corresponding ``torch.dtype`` |
  +==================+===============================+
  | :class:`int`     | :attr:`~torch.int64`          |
  +------------------+-------------------------------+
  | :class:`float`   | :attr:`~torch.float64`        |
  +------------------+-------------------------------+
  | :class:`complex` | :attr:`~torch.complex64`      |
  +------------------+-------------------------------+
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code: def _process_inputs(
      self, actual: Any, expected: Any, *, id: Tuple[Any, ...]
  ) -> Tuple[Union[int, float, complex], Union[int, float, complex]]:
      self._check_inputs_isinstance(
          actual, expected, cls=self._supported_types
      )
      actual, expected = (
          self._to_number(number_like, id=id)
          for number_like in (actual, expected)
      )
      return actual, expected
  
imports: import abc
  import cmath
  import collections.abc
  import contextlib
  import warnings
  from typing import Any, Callable, Collection, Dict, List, NoReturn, Optional, Sequence, Tuple, Type, Union
  import torch
label_desc: Use image node_id 3 for calling the NumberPair obj's underlying member method code with example usage: obj._process_inputs(actual, expected) and returns: actual,  expected