Problem $2$: This problem has $3$ parts. Please complete all $3$ partsTesla is testing a new algorithm designed to detect low lying obstacles under challenging weather conditions in its self $-$ driving car model with a probability of $0\mathrm{.}90$However, the design engineers expect this probability to vary with the severity of weather conditions. Tesla's computer system automatically records the weather condition each time a low $-$ lying obstacle is detected. Based on a series of controlled tests, in which a low $-$ lying obstacle was placed on the road under various severe weather conditions, the following information is available: Given the low lying obstacle was, in fact, detected by Tesla's computer system, the weather was cloudy $75\%$ of the time, rainy $20\%$ of the time, and snowy $5\%$ of$1$ the time. When the system failed to detect the low $-$ lying obstacLe on the road, $60\%$ of the days were cloudy, $30\%$ rainy, and $10\%$ snowy.A$)$ Use this information to find the probability of Tesla's model detecting a low $-$ lying obstacle given rainy weather conditions with Bayes Rule.B$)$ Copy and paste a screen shot of Chat GPT solution. In case chat gpt failed to give you a solution, it is appropriate to post that screen shot hereC$)$ Compare your problem $-$ solving strategy with Chat GPT$\text{'}$s strategy? Are they consistent?