answer:The average global temperature for the past 20 years, based on the data provided in the table, is 0.7145 degrees Celsius. This is the result of calling the `calculateAverageGlobalTemperature()` function provided in the PHP code you provided.

answer:"Have you visited our restaurant lately? We hope you had a delicious meal and an enjoyable experience. We value your feedback and would love to hear about your visit! Please take a moment to leave us a review on Yelp or Google. Your thoughts and opinions will help us to continue providing excellent service and dishes. Thank you for choosing to dine with us!" #leavereview #restaurantlove #foodiefeedback

answer:Sure, here are two limericks in LaTeX format: documentclass{article} usepackage{amsmath} begin{document} textbf{Limerick 1: Mathematician} begin{verse} There once was a math whiz so bright, Solving problems with all of his might. He'd integrate and derive, And never contrive, For his solutions were always just right. end{verse} textbf{Limerick 2: Computer Scientist} begin{verse} There once was a coder so keen, Writing programs that were quite obscene. With syntax so clean, And algorithms so mean, Their code was the best that's been seen. end{verse} end{document} Note that the "verse" environment is used to typeset the limericks, and the "textbf" command is used to make the titles bold.

answer:There are several ways to leverage a GPT-2 model to generate text while ensuring that the output adheres to a prescribed set of stylistic guidelines or aligns with a predetermined tone or theme. One approach is to fine-tune the GPT-2 model on a specific dataset that reflects the desired style or tone. This process involves training the model on a corpus of text that is similar to the desired output, and then fine-tuning the model to generate text that adheres to the desired style or tone. Another approach is to use a technique called conditional generation, where the GPT-2 model is given a set of input parameters that specify the desired style or tone. For instance, if the desired style is formal, the input parameters could include a list of formal vocabulary words, sentence structures, and tone markers. The GPT-2 model can then be trained to generate text that adheres to these input parameters. Additionally, one can use a technique called style transfer, which involves taking a piece of text and converting it into a different style or tone. This approach involves training the GPT-2 model on a corpus of text in the desired style or tone, and then using the model to generate text that is similar in content but in the desired style or tone. Overall, the key to achieving this task is to provide the GPT-2 model with enough training data and input parameters that reflect the desired style or tone. By fine-tuning the model, using conditional generation, or style transfer, it is possible to generate text that adheres to a prescribed set of stylistic guidelines or aligns with a predetermined tone or theme.