How to use the snoop.config function in snoop

import torch
import numpy
import math
import sys
import torchsnooper
from python_toolbox import sys_tools
import re
import snoop
import copy


ansi_escape = re.compile(r'\x1B\[[0-?]*[ -/]*[@-~]')
default_config = copy.copy(snoop.config)


def func():
    x = torch.tensor(math.inf)
    x = torch.tensor(math.nan)
    x = torch.tensor(1.0, requires_grad=True)
    x = torch.tensor([1.0, math.nan, math.inf])
    x = numpy.zeros((2, 2))
    x = (x, x)


verbose_expect = '''
01:24:31.56 >>> Call to func in File "test_snoop.py", line 16
01:24:31.56   16 | def func():
01:24:31.56   17 |     x = torch.tensor(math.inf)
01:24:31.56 .......... x = tensor<(), float32, cpu, has_inf>

def assert_output(verbose, expect):
    torchsnooper.register_snoop(verbose=verbose)
    with sys_tools.OutputCapturer(stdout=False, stderr=True) as output_capturer:
        assert sys.gettrace() is None
        snoop(func)()
        assert sys.gettrace() is None
    output = output_capturer.string_io.getvalue()
    output = ansi_escape.sub('', output)
    assert clean_output(output) == clean_output(expect)
    snoop.config = default_config

unwanted = {
        snoop.configuration.len_shape_watch,
        snoop.configuration.dtype_watch,
    }
    snoop.config.watch_extras = tuple(x for x in snoop.config.watch_extras if x not in unwanted)
    if verbose:

        class TensorWrap:

            def __init__(self, tensor):
                self.tensor = tensor

            def __repr__(self):
                return self.tensor.__repr__()

        snoop.config.watch_extras += (
            lambda source, value: ('{}.data'.format(source), TensorWrap(value.data)),
        )

def register_snoop(verbose=False, tensor_format=default_format, numpy_format=default_numpy_format):
    import snoop
    import cheap_repr
    import snoop.configuration
    cheap_repr.register_repr(torch.Tensor)(lambda x, _: tensor_format(x))
    cheap_repr.register_repr(numpy.ndarray)(lambda x, _: numpy_format(x))
    cheap_repr.cheap_repr(torch.zeros(6))
    unwanted = {
        snoop.configuration.len_shape_watch,
        snoop.configuration.dtype_watch,
    }
    snoop.config.watch_extras = tuple(x for x in snoop.config.watch_extras if x not in unwanted)
    if verbose:

        class TensorWrap:

            def __init__(self, tensor):
                self.tensor = tensor

            def __repr__(self):
                return self.tensor.__repr__()

        snoop.config.watch_extras += (
            lambda source, value: ('{}.data'.format(source), TensorWrap(value.data)),
        )