Youtube Download Partial Video

[Maren Ruminski]

Returnsthe same as lru_cache(maxsize=None), creating a thinwrapper around a dictionary lookup for the function arguments. Because itnever needs to evict old values, this is smaller and faster thanlru_cache() with a size limit.

Transform a method of a class into a property whose value is computed onceand then cached as a normal attribute for the life of the instance. Similarto property(), with the addition of caching. Useful for expensivecomputed properties of instances that are otherwise effectively immutable.

The cached_property does not prevent a possible race condition inmulti-threaded usage. The getter function could run more than once on thesame instance, with the latest run setting the cached value. If the cachedproperty is idempotent or otherwise not harmful to run more than once on aninstance, this is fine. If synchronization is needed, implement the necessarylocking inside the decorated getter function or around the cached propertyaccess.

If a mutable mapping is not available or if space-efficient key sharing isdesired, an effect similar to cached_property() can also be achieved bystacking property() on top of lru_cache(). SeeHow do I cache method calls? for more details on how this differs from cached_property().

Changed in version 3.12: Prior to Python 3.12, cached_property included an undocumented lock toensure that in multi-threaded usage the getter function was guaranteed torun only once per instance. However, the lock was per-property, notper-instance, which could result in unacceptably high lock contention. InPython 3.12+ this locking is removed.

A comparison function is any callable that accepts two arguments, compares them,and returns a negative number for less-than, zero for equality, or a positivenumber for greater-than. A key function is a callable that accepts oneargument and returns another value to be used as the sort key.

Decorator to wrap a function with a memoizing callable that saves up to themaxsize most recent calls. It can save time when an expensive or I/O boundfunction is periodically called with the same arguments.

If typed is set to true, function arguments of different types will becached separately. If typed is false, the implementation will usuallyregard them as equivalent calls and only cache a single result. (Sometypes such as str and int may be cached separately even when typedis false.)

The wrapped function is instrumented with a cache_parameters()function that returns a new dict showing the values for maxsizeand typed. This is for information purposes only. Mutating the valueshas no effect.

To help measure the effectiveness of the cache and tune the maxsizeparameter, the wrapped function is instrumented with a cache_info()function that returns a named tuple showing hits, misses,maxsize and currsize.

Given a class defining one or more rich comparison ordering methods, thisclass decorator supplies the rest. This simplifies the effort involvedin specifying all of the possible rich comparison operations:

While this decorator makes it easy to create well behaved totallyordered types, it does come at the cost of slower execution andmore complex stack traces for the derived comparison methods. Ifperformance benchmarking indicates this is a bottleneck for a givenapplication, implementing all six rich comparison methods instead islikely to provide an easy speed boost.

This decorator makes no attempt to override methods that have beendeclared in the class or its superclasses. Meaning that if asuperclass defines a comparison operator, total_ordering will notimplement it again, even if the original method is abstract.

Return a new partial object which when calledwill behave like func called with the positional arguments argsand keyword arguments keywords. If more arguments are supplied to thecall, they are appended to args. If additional keyword arguments aresupplied, they extend and override keywords.Roughly equivalent to:

When func is a non-descriptor callable, an appropriate bound method iscreated dynamically. This behaves like a normal Python function whenused as a method: the self argument will be inserted as the firstpositional argument, even before the args and keywords supplied tothe partialmethod constructor.

To add overloaded implementations to the function, use the register()attribute of the generic function, which can be used as a decorator. Forfunctions annotated with types, the decorator will infer the type of thefirst argument automatically:

Where there is no registered implementation for a specific type, itsmethod resolution order is used to find a more generic implementation.The original function decorated with @singledispatch is registeredfor the base object type, which means it is used if no betterimplementation is found.

To define a generic method, decorate it with the @singledispatchmethoddecorator. When defining a function using @singledispatchmethod, notethat the dispatch happens on the type of the first non-self or non-clsargument:

@singledispatchmethod supports nesting with other decorators such as@classmethod. Note that to allow fordispatcher.register, singledispatchmethod must be the outer mostdecorator. Here is the Negator class with the neg methods bound tothe class, rather than an instance of the class:

To allow access to the original function for introspection and other purposes(e.g. bypassing a caching decorator such as lru_cache()), this functionautomatically adds a __wrapped__ attribute to the wrapper that refers tothe function being wrapped.

The main intended use for this function is in decorator functions whichwrap the decorated function and return the wrapper. If the wrapper function isnot updated, the metadata of the returned function will reflect the wrapperdefinition rather than the original function definition, which is typically lessthan helpful.

update_wrapper() may be used with callables other than functions. Anyattributes named in assigned or updated that are missing from the objectbeing wrapped are ignored (i.e. this function will not attempt to set themon the wrapper function). AttributeError is still raised if thewrapper function itself is missing any attributes named in updated.

partial objects are like function objects in that they arecallable, weak referenceable, and can have attributes. There are some importantdifferences. For instance, the __name__ and __doc__ attributesare not created automatically. Also, partial objects defined inclasses behave like static methods and do not transform into bound methodsduring instance attribute look-up.

When loading a partial config you have 3 options: replace, merge, append. I can't find a description anywhere as to what exactly each of these does! Especially between merge and append. I did see this KB article but it really doesn't explain the ramifications for each of these choice and neither does the CLI Guide.

I'm doing an AppID optimization project using the Migration Tool 3.3.15 which does not export via API to PANOS 8. Well, it does, it just can't parse the security policies and commit. So, I'm going to import the changes manually using load partial config. All I have to do is remove unused objects, create 1 new service-group and update the security rulebase.

Append: This would put everything in the security stanza in the file x.xml at the end of the existing ruleset and not overwrite what's there. If this is true, what would happen if you had an entry that had the same name? Would it just update it or overwrite it? Generate an error?

God partial config is such a crap feature. Sorry, but it's use case is extremely limited, and I don't recommend you utilize it unless you really know what you are doing. I've ran into so many situations where an admin has tried to merge security rules and havene't properly dealt with zones, objects, or anything else these rules rely on and then for some reason find it acceptable to force commit when they run into issue.

If you are looking to do something like this, I really recommend modifying the config directly through the XML. At least this way you have to actually look at what you are doing and can validate the config off the box.

This essentially acts as a replace, but it won't delete any entries that aren't present and if entries exist with duplicate entry names the rule will simply be combined. Usually Merge is 100% what you would use during a normal partial config instead of the 'replace' or 'append' function. This issue with this is that Merge can have unintended actions if you have two seperate entries with the same name and don't actually realize that until after the fact.

Does exactly what you already guessed. If you target /devices/localhost.localdomain/vsys/vsys1/rulebase/security and replace then it'll remove anything already present and input whatever you have. Useful if you need to do a very quick migration or if two firewalls are switching locations within the network for some reason.

The Migration Tool should be able to export via the API, if you are recieving API errors (sometimes a space on the end of a rule or something similar) you will likely recieve these same errors using the load config partial.

I assume you are going to load all rules back (the original port based and the new app based) In that case I would recommend removing all security policies in the GUI (do NOT commit) then load config partial with append

Thanks for the recommendation, but what do each of these options actually do? What is there affect? That's what I'm trying to figure out because the documentation doesn't describe what exactly, merge, append and replace actually do and when to use each.

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