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If you have ever written a blog, paper or dissertation, you know the amount of time and energy that goes into producing something worthwhile. The power of ChatGPT lies in its speed to produce well-written content in seconds and its ability to take complicated topics and simplify them.




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This article was a summary of ChatGPT, but if you would like to know more in-depth knowledge about ChatGPT on a technical level, I would recommend reading the research paper Language Models are Few-Shot Learners. Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.


Once the debt ceiling reaches $31.381 trillion, the U.S. government will not be able to issue any new debt. In the past, the government has been able to continue to meet its obligations in the short term using extraordinary measures, which provide for shifting certain funds around such as suspending the sale of certain government securities. Past analyses of the debt limit have shown that a reduced need to issue new debt following tax revenues collected in March and April could also postpone the X date, making it very uncertain.


To be sure, political advantage should not be sought by threatening default, and the debt ceiling must be raised or suspended. Lawmakers must not jeopardize the full faith and credit of the U.S. government. At the same time, the need to raise the debt ceiling can serve as a useful moment for taking stock of our fiscal state and for pursuing revenue increases, entitlement reform, and/or spending reductions.


The Balanced Budget and Emergency Deficit Control Reaffirmation Act of 1987: This bill, also known as Gramm-Rudman-Hollings II, was passed to correct constitutional deficiencies in the 1985 Gramm-Rudman-Hollings Act. Like its predecessor, GRH II attached a deficit reduction measure to the increased debt limit, requiring automatic sequester if deficits did not meet annual targets.


Line Item Veto Act of 1996: The Line Item Veto Act of 1996 gave the President authority to veto specific provisions in legislation that increased the federal deficit, increased entitlement spending over the baseline, created tax benefits, or allocated discretionary budget authority. This practice, known as a line-item veto, was ruled unconstitutional by the Supreme Court for violating the separation of powers clause by allowing the President to amend a statute without Congress voting on it. While the 1996 line-item veto was found unconstitutional, other versions of it have been presented, including one by President Bush that would have allowed him to cancel spending obligations using his existing rescission authority.


Statutory PAYGO Act of 2010: The Statutory PAYGO Act of 2010 contained a debt limit increase of $1.9 trillion, the largest nominal increase ever enacted until that point in time. In exchange for the debt limit increase, this legislation included a budget process reform that reinstituted statutory PAYGO procedures that require tax cuts and mandatory spending increases to be fully offset (with some exemptions). Informally, the agreement to raise the debt ceiling also led to the creation of a National Commission on Fiscal Responsibility and Reform (also known as the Simpson-Bowles commission).


Some of these regions overlap with the areas of tenderness traditionally known as tender points or trigger points. However, some of these previously noted areas of tenderness are not included in the regions of pain.


Chat bots like GPT are powered by large amounts of data and computing techniques to make predictions to string words together in a meaningful way. They not only tap into a vast amount of vocabulary and information, but also understand words in context. This helps them mimic speech patterns while dispatching an encyclopedic knowledge.


Research is required to investigate strategies to maintain and improve tissue perfusion and organ function while mitigating FAS. This includes the need to identify sensitive and accurate markers of intravascular volume status. Further research should also identify the optimal rates of fluid removal in specific patient groups, tools to predict the consequences of fluid removal, and investigate the role of adjunctive therapies (e.g. albumin) and vasopressors to support fluid removal versus the harm from prolonged fluid accumulation. Aggregation and integration of large-scale (realworld) physiological and clinical data will lend itself to data science techniques such as artificial intelligence and machine learning that can in real-time accurately measure FAS and account for patient characteristics such as illness severity and chronic co-morbidities to identify the optimal method, timing and rate of fluid removal for individual patients.


Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit -nc/4.0/.


We use comments to specify parts of the program that should be simply ignored and not executed by the Python interpreter. This means that everything written inside a comment is not taken into consideration at all and you can write in any language that you want, including your own native language.


As you can see, this initialization is quite similar to the ones when we had integer numbers. Here we are only changing the value on the right. The Python interpreter is smart enough to know that we are dealing with another type of variable, namely a float type of variable.


Getting elements of a list using indexes is simple. Finding indexes of elements of a list is also easy. We simply need to use the method index() and mention the element that we want to find inside a list:


Sorting elements in a list is something that you may need to do from time to time. sort() is a built-in method that makes it possible for you to sort elements in a list in an ascending order alphabetically or numerically:


Now, you can pick up your phone and check its corresponding meaning in English using Google Translate or any other application of your choice. But if you were to use a physical dictionary, you would need to find this word by going to that specific page and check its meaning sitting right beside it. The reference or the key for the meaning of this word would be the term Wasser.


To help us with that, we are going to need to use a method called items() that converts a dictionary into a list of tuples. The element in index 0 is a key, whereas in position with index 1, we have a value.


If you remember high school math lessons, you should already know about union, intersection, and the difference between two sets of elements. These operations are supported for sets in Python as well.


Sometimes we may need to exit a for loop before it reaches the end. This may be the case when a condition has been met or we have found what we were looking for and there is no need to continue any further.


To define a method in Python, we start by using the def keyword, then the name of the function and then a list of arguments that we expect to be used. After that, we need to start writing the body of the method in a new line after an indentation.


We do not use the exact same blueprint for every object that is out there. There is a blueprint for producing books, another one for producing pencils, and so on. We need to categorize them based on attributes and their functionalities.


We can call them by using both the class name and an object that is created by that class where this method is declared. This means that they do not need to have their first argument related to the object or class calling them (as was the case with using parameters self for instance methods and cls for class methods).


When we work on our own programs, we may need to not let everyone know about all the details that our classes have. So we can limit access to them, so that certain attributes are less likely to be accessed unintentionally and be modified wrongfully.


Many developers around the world are generous with their time and knowledge and publish a lot of really useful libraries. These libraries can save us plenty of time both in our professional work, but also on our side projects that we may do for fun.


We need to write inside the try block the part of the code that we expect is going to throw errors. We then catch those types of errors inside the except block by also specifying the type of error that we except to happen.


Then, we try to check whether we are dividing by zero. If this error is thrown, then this except block is going to take over the execution and everything that is inside it is going to be executed. Similarly, we continue with the rest of the errors declared.


We can also put more than one error inside parenthesis to catch multiple exceptions. But this is not going to be helpful for us, since we do not know what specific error has been thrown. In other words, the following method does work, but it is not recommended:


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