Develop Reference

Mocking __init__ of a class imported in file

I have a file copy_x_to_y.py which goes like this:
from abcd import F
def function_to_be_tested():
F()
in abcd.py file, i have something like this:
from xyz import XY
class F():
def __init__(self, arg1):
self.xy = XY(arg1)
I want to mock init of XY in my test case.
I have tried mocking F's init with:
def mock_func(*args, **kwargs):
pass
#patch('path/to/copy_x_to_y.F.__init__', mock_func)
def test():
assert function_to_be_tested() is None
but it always happens to call XY's init, resulting in error as its initialization calls
to connect with S3 with arg1. How to test this kind of structure?

What is the reason for wanting to mock __init__ of XY? Do you want it to return a specific object of XY, do you want to check if XY.__init__ was called with specific arguments or something else?
A possible solution to your problem would be to mock the entire class, but have it return a "normal" object. Here's an example:
>>> from unittest.mock import patch
>>> class MyClass:
... def __init__(self, val):
... self._val = val
... def foo(self):
... print(self._val)
...
>>> a = MyClass(1)
>>> a.foo()
1
>>> patcher = patch('__main__.MyClass', return_value=a)
>>> mock_class = patcher.start()
>>> b = MyClass(2) # This will return a.
>>> b.foo()
1
>>> mock_class.call_args_list
[call(2)]
>>> patcher.stop()
Which in your case would be:
from xyz import XY
from path/to/copy_x_to_y import function_to_be_tested
def test():
arg1 = ...
a = XY(arg1) # Has to be called before the patch to get a "normal" object.
with patch('xyz.XY', return_value=a) as mock_xy:
# Run funcion to be tested here and check results.
function_to_be_tested()
assert ...
Some side notes:
It is possible to mock __init__ directly though, if that's really what you need to do.
>>> def my_init(self, *args, **kwargs):
... self._val = 1
>>> patcher = patch.object(MyClass, '__init__', my_init)
>>> mock_init = patcher.start()
>>> a = MyClass(2)
>>> a.foo()
1
If you use the patch decorator, you have to supply the decorated function with one extra argument that is the mock of the class or object.
https://docs.python.org/3/library/unittest.mock.html#unittest.mock.patch
#patch('path/to/SomeClass', ...)
def test(mock_class):
...
Also patch is typically (exclusively?) used to patch a class while patch.object is used to patch a member inside a class or module. https://docs.python.org/3/library/unittest.mock.html#unittest.mock.patch.object

How to print method name of dynamically assigned function?

When I run the following piece of code, only the print statements in the method, which I have dynamically assigned to the class "Test" only return "my_unique_method_name".
How to print the method name I have given it? (Which is "wizardry" in the method—see "Expected Output" below.)
#!/usr/bin/python3
import sys
import inspect
class Test:
pass
def my_unique_method_name(self):
print(inspect.stack()[0][3])
print(sys._getframe().f_code.co_name)
print(inspect.currentframe().f_code.co_name)
Test.wizardry = my_unique_method_name
t = Test()
t.wizardry()
Current Output
my_unique_method_name
my_unique_method_name
my_unique_method_name
Expected Output
wizardry

The name of the function is "my_unique_method_name" - the function is an object with a property which is its name.
Test.wizardy is just a variable, and its name is unrelated to the function which is its value.
It's the same siutation as these:
>>> def unique(): pass
...
>>> unique
<function unique at 0x0000000002CA4F28>
>>> unique.__name__
'unique'
>>> f = unique
>>> f.__name__
'unique'
>>> class A: pass
...
>>> a = A()
>>> a.g = unique
>>> a.g
<function unique at 0x0000000002CA4F28>
>>> a.g.__name__
'unique'

You could do it by examining the contents of self.__class__.__dict__:
class Test:
pass
def my_unique_method_name(self):
for k, v in self.__class__.__dict__.items():
if v == my_unique_method_name:
print(k)
break # Assume there's only one.
Test.wizardry = my_unique_method_name
t = Test()
t.wizardry() # -> wizardry
Here's a more generic (and thus reusable) way of doing it that doesn't require the function assigned to do anything special—it's also likely to be a little faster since there's no for loop:
from functools import wraps
class Test:
pass
def my_unique_method_name(self):
pass
def assign_function_to_method(func, cls, name):
#wraps(func) # Optional (but recommended).
def wrapped(*args, **kwargs):
print(name)
return func(*args, **kwargs)
setattr(cls, name, wrapped)
assign_function_to_method(my_unique_method_name, Test, 'wizardry')
t = Test()
t.wizardry() # -> wizardry
Either approach works in both Python 2 and 3.

How can I delay the __init__ call until an attribute is accessed?

I have a test framework that requires test cases to be defined using the following class patterns:
class TestBase:
def __init__(self, params):
self.name = str(self.__class__)
print('initializing test: {} with params: {}'.format(self.name, params))
class TestCase1(TestBase):
def run(self):
print('running test: ' + self.name)
When I create and run a test, I get the following:
>>> test1 = TestCase1('test 1 params')
initializing test: <class '__main__.TestCase1'> with params: test 1 params
>>> test1.run()
running test: <class '__main__.TestCase1'>
The test framework searches for and loads all TestCase classes it can find, instantiates each one, then calls the run method for each test.
load_test(TestCase1(test_params1))
load_test(TestCase2(test_params2))
...
load_test(TestCaseN(test_params3))
...
for test in loaded_tests:
test.run()
However, I now have some test cases for which I don't want the __init__ method called until the time that the run method is called, but I have little control over the framework structure or methods. How can I delay the call to __init__ without redefining the __init__ or run methods?
Update
The speculations that this originated as an XY problem are correct. A coworker asked me this question a while back when I was maintaining said test framework. I inquired further about what he was really trying to achieve and we figured out a simpler workaround that didn't involve changing the framework or introducing metaclasses, etc.
However, I still think this is a question worth investigating: if I wanted to create new objects with "lazy" initialization ("lazy" as in lazy evaluation generators such as range, etc.) what would be the best way of accomplishing it? My best attempt so far is listed below, I'm interested in knowing if there's anything simpler or less verbose.

First Solution：use property.the elegant way of setter/getter in python.
class Bars(object):
def __init__(self):
self._foo = None
#property
def foo(self):
if not self._foo:
print("lazy initialization")
self._foo = [1,2,3]
return self._foo
if __name__ == "__main__":
f = Bars()
print(f.foo)
print(f.foo)
Second Solution:the proxy solution,and always implement by decorator.
In short, Proxy is a wrapper that wraps the object you need. Proxy could provide additional functionality to the object that it wraps and doesn't change the object's code. It's a surrogate which provide the abitity of control access to a object.there is the code come form user Cyclone.
class LazyProperty:
def __init__(self, method):
self.method = method
self.method_name = method.__name__
def __get__(self, obj, cls):
if not obj:
return None
value = self.method(obj)
print('value {}'.format(value))
setattr(obj, self.method_name, value)
return value
class test:
def __init__(self):
self._resource = None
#LazyProperty
def resource(self):
print("lazy")
self._resource = tuple(range(5))
return self._resource
if __name__ == '__main__':
t = test()
print(t.resource)
print(t.resource)
print(t.resource)
To be used for true one-time calculated lazy properties. I like it because it avoids sticking extra attributes on objects, and once activated does not waste time checking for attribute presence

Metaclass option
You can intercept the call to __init__ using a metaclass. Create the object with __new__ and overwrite the __getattribute__ method to check if __init__ has been called or not and call it if it hasn't.
class DelayInit(type):
def __call__(cls, *args, **kwargs):
def init_before_get(obj, attr):
if not object.__getattribute__(obj, '_initialized'):
obj.__init__(*args, **kwargs)
obj._initialized = True
return object.__getattribute__(obj, attr)
cls.__getattribute__ = init_before_get
new_obj = cls.__new__(cls, *args, **kwargs)
new_obj._initialized = False
return new_obj
class TestDelayed(TestCase1, metaclass=DelayInit):
pass
In the example below, you'll see that the init print won't occur until the run method is executed.
>>> new_test = TestDelayed('delayed test params')
>>> new_test.run()
initializing test: <class '__main__.TestDelayed'> with params: delayed test params
running test: <class '__main__.TestDelayed'>
Decorator option
You could also use a decorator that has a similar pattern to the metaclass above:
def delayinit(cls):
def init_before_get(obj, attr):
if not object.__getattribute__(obj, '_initialized'):
obj.__init__(*obj._init_args, **obj._init_kwargs)
obj._initialized = True
return object.__getattribute__(obj, attr)
cls.__getattribute__ = init_before_get
def construct(*args, **kwargs):
obj = cls.__new__(cls, *args, **kwargs)
obj._init_args = args
obj._init_kwargs = kwargs
obj._initialized = False
return obj
return construct
#delayinit
class TestDelayed(TestCase1):
pass
This will behave identically to the example above.

In Python, there is no way that you can avoid calling __init__ when you instantiate a class cls. If calling cls(args) returns an instance of cls, then the language guarantees that cls.__init__ will have been called.
So the only way to achieve something similar to what you are asking is to introduce another class that will postpone the calling of __init__ in the original class until an attribute of the instantiated class is being accessed.
Here is one way:
def delay_init(cls):
class Delay(cls):
def __init__(self, *arg, **kwarg):
self._arg = arg
self._kwarg = kwarg
def __getattribute__(self, name):
self.__class__ = cls
arg = self._arg
kwarg = self._kwarg
del self._arg
del self._kwarg
self.__init__(*arg, **kwarg)
return getattr(self, name)
return Delay
This wrapper function works by catching any attempt to access an attribute of the instantiated class. When such an attempt is made, it changes the instance's __class__ to the original class, calls the original __init__ method with the arguments that were used when the instance was created, and then returns the proper attribute. This function can be used as decorator for your TestCase1 class:
class TestBase:
def __init__(self, params):
self.name = str(self.__class__)
print('initializing test: {} with params: {}'.format(self.name, params))
class TestCase1(TestBase):
def run(self):
print('running test: ' + self.name)
>>> t1 = TestCase1("No delay")
initializing test: <class '__main__.TestCase1'> with params: No delay
>>> t2 = delay_init(TestCase1)("Delayed init")
>>> t1.run()
running test: <class '__main__.TestCase1'>
>>> t2.run()
initializing test: <class '__main__.TestCase1'> with params: Delayed init
running test: <class '__main__.TestCase1'>
>>>
Be careful where you apply this function though. If you decorate TestBase with delay_init, it will not work, because it will turn the TestCase1 instances into TestBase instances.

In my answer I'd like to focus on cases when one wants to instantiate a class whose initialiser (dunder init) has side effects. For instance, pysftp.Connection, creates an SSH connection, which may be undesired until it's actually used.
In a great blog series about conceiving of wrapt package (nit-picky decorator implementaion), the author describes Transparent object proxy. This code can be customised for the subject in question.
class LazyObject:
_factory = None
'''Callable responsible for creation of target object'''
_object = None
'''Target object created lazily'''
def __init__(self, factory):
self._factory = factory
def __getattr__(self, name):
if not self._object:
self._object = self._factory()
return getattr(self._object, name)
Then it can be used as:
obj = LazyObject(lambda: dict(foo = 'bar'))
obj.keys() # dict_keys(['foo'])
But len(obj), obj['foo'] and other language constructs which invoke Python object protocols (dunder methods, like __len__ and __getitem__) will not work. However, for many cases, which are limited to regular methods, this is a solution.
To proxy object protocol implementations, it's possible to use neither __getattr__, nor __getattribute__ (to do it in a generic way). The latter's documentation notes:
This method may still be bypassed when looking up special methods as the result of implicit invocation via language syntax or built-in functions. See Special method lookup.
As a complete solution is demanded, there are examples of manual implementations like werkzeug's LocalProxy and django's SimpleLazyObject. However a clever workaround is possible.
Luckily there's a dedicated package (based on wrapt) for the exact use case, lazy-object-proxy which is described in this blog post.
from lazy_object_proxy import Proxy
obj = Proxy(labmda: dict(foo = 'bar'))
obj.keys() # dict_keys(['foo'])
len(len(obj)) # 1
obj['foo'] # 'bar'

One alternative would be to write a wrapper that takes a class as input and returns a class with delayed initialization until any member is accessed. This could for example be done as this:
def lazy_init(cls):
class LazyInit(cls):
def __init__(self, *args, **kwargs):
self.args = args
self.kwargs = kwargs
self._initialized = False
def __getattr__(self, attr):
if not self.__dict__['_initialized']:
cls.__init__(self,
*self.__dict__['args'], **self.__dict__['kwargs'])
self._initialized = True
return self.__dict__[attr]
return LazyInit
This could then be used as such
load_test(lazy_init(TestCase1)(test_params1))
load_test(lazy_init(TestCase2)(test_params2))
...
load_test(lazy_init(TestCaseN)(test_params3))
...
for test in loaded_tests:
test.run()

Answering your original question (and the problem I think you are actually trying to solve), "How can I delay the init call until an attribute is accessed?": don't call init until you access the attribute.
Said another way: you can make the class initialization simultaneous with the attribute call. What you seem to actually want is 1) create a collection of TestCase# classes along with their associated parameters; 2) run each test case.
Probably your original problem came from thinking you had to initialize all your TestCase classes in order to create a list of them that you could iterate over. But in fact you can store class objects in lists, dicts etc. That means you can do whatever method you have for finding all TestCase classes and store those class objects in a dict with their relevant parameters. Then just iterate that dict and call each class with its run() method.
It might look like:
tests = {TestCase1: 'test 1 params', TestCase2: 'test 2 params', TestCase3: 'test 3 params'}
for test_case, param in tests.items():
test_case(param).run()

Overridding __new__
You could do this by overriding __new__ method and replacing __init__ method with a custom function.
def init(cls, real_init):
def wrapped(self, *args, **kwargs):
# This will run during the first call to `__init__`
# made after `__new__`. Here we re-assign the original
# __init__ back to class and assign a custom function
# to `instances.__init__`.
cls.__init__ = real_init
def new_init():
if new_init.called is False:
real_init(self, *args, **kwargs)
new_init.called = True
new_init.called = False
self.__init__ = new_init
return wrapped
class DelayInitMixin(object):
def __new__(cls, *args, **kwargs):
cls.__init__ = init(cls, cls.__init__)
return object.__new__(cls)
class A(DelayInitMixin):
def __init__(self, a, b):
print('inside __init__')
self.a = sum(a)
self.b = sum(b)
def __getattribute__(self, attr):
init = object.__getattribute__(self, '__init__')
if not init.called:
init()
return object.__getattribute__(self, attr)
def run(self):
pass
def fun(self):
pass
Demo:
>>> a = A(range(1000), range(10000))
>>> a.run()
inside __init__
>>> a.a, a.b
(499500, 49995000)
>>> a.run(), a.__init__()
(None, None)
>>> b = A(range(100), range(10000))
>>> b.a, b.b
inside __init__
(4950, 49995000)
>>> b.run(), b.__init__()
(None, None)
Using cached properties
The idea is to do the heavy calculation only once by caching results. This approach will lead to much more readable code if the whole point of delaying initialization is improving performance.
Django comes with a nice decorator called #cached_property. I tend to use it a lot in both code and unit-tests for caching results of heavy properties.
A cached_property is a non-data descriptor. Hence once the key is set in instance's dictionary, the access to property would always get the value from there.
class cached_property(object):
"""
Decorator that converts a method with a single self argument into a
property cached on the instance.
Optional ``name`` argument allows you to make cached properties of other
methods. (e.g. url = cached_property(get_absolute_url, name='url') )
"""
def __init__(self, func, name=None):
self.func = func
self.__doc__ = getattr(func, '__doc__')
self.name = name or func.__name__
def __get__(self, instance, cls=None):
if instance is None:
return self
res = instance.__dict__[self.name] = self.func(instance)
return res
Usage:
class A:
#cached_property
def a(self):
print('calculating a')
return sum(range(1000))
#cached_property
def b(self):
print('calculating b')
return sum(range(10000))
Demo:
>>> a = A()
>>> a.a
calculating a
499500
>>> a.b
calculating b
49995000
>>> a.a, a.b
(499500, 49995000)

I think you can use a wrapper class to hold the real class you want to instance, and use call __init__ yourself in your code, like(Python 3 code):
class Wrapper:
def __init__(self, cls):
self.cls = cls
self.instance = None
def your_method(self, *args, **kwargs):
if not self.instance:
self.instnace = cls()
return self.instance(*args, **kwargs)
class YourClass:
def __init__(self):
print("calling __init__")
but it's a dump way, but without any trick.

Assert that derived class methods are called in correct order

I'm trying to verify that the implementation of Base.run_this calls the methods of derived class (derived_method_[1st|2nd|3rd]) in correct order. As the output shows, the test is not working. How can I fix this?
class Base(object):
__metaclass__ = abc.ABCMeta
def __init__(self, parameters):
self.parameters = parameters;
#abc.abstractmethod
def must_implement_this(self):
return
def run_this(self):
self.must_implement_this()
if(self.parameters):
first = getattr(self, "derived_method_1st")
first()
second = getattr(self, "derived_method_2nd")
second()
third = getattr(self, "derived_method_3rd")
third()
class Derived(Base):
def must_implement_this(self):
pass
def derived_method_1st(self):
pass
def derived_method_2nd(self):
pass
def derived_method_3rd(self):
pass
mocked = MagicMock(wraps=Derived(True))
mocked.run_this()
mocked.assert_has_calls([call.derived_method_1st(), call.derived_method_2nd(), call.derived_method_3rd()])
Output
AssertionError: Calls not found.
Expected: [call.derived_method_1st(), call.derived_method_2nd(), call.derived_method_3rd()]
Actual: [call.run_this()]

wraps doesn't work well with instances. What happens here is that mocked.run_this returns a new mock object that 'wraps' Derived(True).run_this, where the latter is a bound method to the original Derived() instance.
As such, that method will call self.derived_method_* methods that are bound to that original instance, not to the mock.
You could patch in the run_this method on a spec mock instead:
mock = MagicMock(spec=Derived)
instance = mock()
instance.run_this = Derived.run_this.__get__(instance) # bind to mock instead
instance.parameters = True # adjust as needed for the test
instance.run_this()
Demo:
>>> mock = MagicMock(spec=Derived)
>>> instance = mock()
>>> instance.run_this = Derived.run_this.__get__(instance) # bind to mock instead
>>> instance.parameters = True # adjust as needed for the test
>>> instance.run_this()
>>> instance.mock_calls
[call.must_implement_this(),
call.derived_method_1st(),
call.derived_method_2nd(),
call.derived_method_3rd()]

Mocking out methods on any instance of a python class

I want to mock out methods on any instance of some class in the production code in order to facilitate testing. Is there any library in Python which could facilitate this?
Basically, I want to do the following, but in Python (the following code is Ruby, using the Mocha library):
def test_stubbing_an_instance_method_on_all_instances_of_a_class
Product.any_instance.stubs(:name).returns('stubbed_name')
assert_equal 'stubbed_name', SomeClassThatUsesProduct.get_new_product_name
end
The important thing to note from above is that I need to mock it out on the class level, since I'm actually need to mock out methods on an instance created by the thing I'm testing.
Use Case:
I have a class QueryMaker which calls a method on an instance of RemoteAPI. I want to mock out the RemoteAPI.get_data_from_remote_server method to return some constant. How do I do this inside a test without having to put a special case within the RemoteAPI code to check for what environment it's running in.
Example of what I wanted in action:
# a.py
class A(object):
def foo(self):
return "A's foo"
# b.py
from a import A
class B(object):
def bar(self):
x = A()
return x.foo()
# test.py
from a import A
from b import B
def new_foo(self):
return "New foo"
A.foo = new_foo
y = B()
if y.bar() == "New foo":
print "Success!"

Needing to mock out methods when testing is very common and there are lots of tools to help you with it in Python. The danger with "monkey patching" classes like this is that if you don't undo it afterwards then the class has been modified for all other uses throughout your tests.
My library mock, which is one of the most popular Python mocking libraries, includes a helper called "patch" that helps you to safely patch methods or attributes on objects and classes during your tests.
The mock module is available from:
http://pypi.python.org/pypi/mock
The patch decorator can be used as a context manager or as a test decorator. You can either use it to patch out with functions yourself, or use it to automatically patch with Mock objects that are very configurable.
from a import A
from b import B
from mock import patch
def new_foo(self):
return "New foo"
with patch.object(A, 'foo', new_foo):
y = B()
if y.bar() == "New foo":
print "Success!"
This handles the unpatching for you automatically. You could get away without defining the mock function yourself:
from mock import patch
with patch.object(A, 'foo') as mock_foo:
mock_foo.return_value = "New Foo"
y = B()
if y.bar() == "New foo":
print "Success!"

Mock is the way to do it, alright.
It can be a bit tricky to make sure you're patching the instance method on any instances created from the class.
# a.py
class A(object):
def foo(self):
return "A's foo"
# b.py
from a import A
class B(object):
def bar(self):
x = A()
return x.foo()
# test.py
from a import A
from b import B
import mock
mocked_a_class = mock.Mock()
mocked_a_instance = mocked_a_class.return_value
mocked_a_instance.foo.return_value = 'New foo'
with mock.patch('b.A', mocked_a_class): # Note b.A not a.A
y = B()
if y.bar() == "New foo":
print "Success!"
Referenced in the docs, at the para starting "To configure return values on methods of instances on the patched class..."

Easiest way is probably to use a class method. You really should use an instance method, but it's a pain to create those, whereas there's a built-in function that creates a class method. With a class method, your stub will get a reference to the class (rather than the instance) as the first argument, but since it's a stub this probably doesn't matter. So:
Product.name = classmethod(lambda cls: "stubbed_name")
Note that the signature of the lambda must match the signature of the method you're replacing. Also, of course, since Python (like Ruby) is a dynamic language, there is no guarantee that someone won't switch out your stubbed method for something else before you get your hands on the instance, though I expect you will know pretty quickly if that happens.
Edit: On further investigation, you can leave out the classmethod():
Product.name = lambda self: "stubbed_name"
I was trying to preserve the original method's behavior as closely as possible, but it looks like it's not actually necessary (and doesn't preserve the behavior as I'd hoped, anyhow).

I don't know Ruby quite well enough to tell exactly what you're trying to do, but check out the __getattr__ method. If you define it in your class, Python will call it when code tries to access any attribute of your class that isn't otherwise defined. Since you want it to be a method, it will need to create a method on the fly that it returns.
>>> class Product:
... def __init__(self, number):
... self.number = number
... def get_number(self):
... print "My number is %d" % self.number
... def __getattr__(self, attr_name):
... return lambda:"stubbed_"+attr_name
...
>>> p = Product(172)
>>> p.number
172
>>> p.name()
'stubbed_name'
>>> p.get_number()
My number is 172
>>> p.other_method()
'stubbed_other_method'
Also note that __getattr__ needs to not use any other undefined attributes of your class, or else it will be infinitely recursive, calling __getattr__ for the attribute that doesn't exist.
... def __getattr__(self, attr_name):
... return self.x
>>> p.y
Traceback (most recent call last):
#clipped
RuntimeError: maximum recursion depth exceeded while calling a Python object
If this is something you only want to do from your test code, not the production code, then put your normal class definition in the production code file, then in the test code define the __getattr__ method (unbound), and then bind it to the class you want:
#production code
>>> class Product:
... def __init__(self, number):
... self.number = number
... def get_number(self):
... print "My number is %d" % self.number
...
#test code
>>> def __getattr__(self, attr):
... return lambda:"stubbed_"+attr_name
...
>>> p = Product(172)
>>> p.number
172
>>> p.name()
Traceback (most recent call last):
File "<interactive input>", line 1, in <module>
AttributeError: Product instance has no attribute 'name'
>>> Product.__getattr__ = __getattr__
>>> p.name()
'stubbed_name'
I'm not sure how this would react with a class that was already using __getattribute__ (as opposed to __getattr__, __getattribute__ is called for all attributes whether or not they exist).
If you only want to do this for specific methods that already exist, then you could do something like:
#production code
>>> class Product:
... def __init__(self, number):
... self.number = number
... def get_number(self):
... return self.number
...
>>> p = Product(172)
>>> p.get_number()
172
#test code
>>> def get_number(self):
... return "stub_get_number"
...
>>> Product.get_number = get_number
>>> p.get_number()
'stub_get_number'
Or if you really wanted to be elegant, you could create a wrapper function to make doing multiple methods easy:
#test code
>>> import functools
>>> def stubber(fn):
... return functools.wraps(fn)(lambda self:"stub_"+fn.__name__)
...
>>> Product.get_number = stubber(Product.get_number)
>>> p.get_number()
'stub_get_number'

#Orignal Class definition - path "module.Product"
class Product:
def method_A(self):
# do something
pass
def method_B(self):
self.random_attr = 1
#Test case
from module import Product
class MockedProduct(Product):
def method_B(self):
self.random_attr = 2
with mock.patch('module.Product', new=MockedProduct):
#Write test case logic here
#Now method_B function call on product class instance should return 2
#instead of 1

minimal reproducible example using pytest and monkeypatch
# a.py
class A(object):
def foo(self):
return "A's foo"
# b.py
from a import A
class B(object):
def bar(self):
x = A()
return x.foo()
# test_ab.py
import pytest
from a import A
from b import B
def new_foo(self):
return "New foo"
def test_mock_instance_method(monkeypatch):
y = B()
print(y.bar())
monkeypatch.setattr(A, 'foo', new_foo)
print(y.bar())
gives you
$ pytest -rP .
============================= test session starts ==============================
platform linux -- Python 3.8.8, pytest-6.2.5, py-1.10.0, pluggy-1.0.0
rootdir: /home/user/220225so
plugins: cov-3.0.0
collected 1 item
test_ab.py . [100%]
==================================== PASSES ====================================
__________________________ test_mock_instance_method ___________________________
----------------------------- Captured stdout call -----------------------------
A's foo
New foo
============================== 1 passed in 0.01s ===============================
$