<prompt> Hlalosa mohopolo oa ho rarahana ha nako.nan  <response>: Instruction in English: Explain the concept of time complexity. . Response in English: Time complexity is a computer science concept that refers to the amount of time a particular algorithm or program takes to execute, in relation to the size of the input data. Its used to evaluate and compare the efficiency of algorithms, as well as to identify potential performance bottlenecks.When we analyze the time complexity of an algorithm, we usually try to estimate the number of basic operations like additions, multiplications, or assignments it performs. Since different computers have different processing speeds, the time complexity of an algorithm is usually expressed in terms of the size of the input rather than in terms of the actual time taken to execute.To quantify time complexity, we use the big O notation, which describes the upper bound of the growth rate of the algorithms running time. For example, if an algorithm takes On time to execute, it means that its running time increases linearly with the size of the input. Other common time complexities include Olog n, On2, and O2n, which correspond to logarithmic, quadratic, and exponential growth rates, respectively.In summary, time complexity is a useful metric for evaluating the performance of algorithms and helps us determine which one is the most suitable for a particular task. By analyzing the time complexity of an algorithm, we can ensure that our programs have acceptable running times, even when dealing with large data sets. . Response in Sesotho: Ho rarahana ha nako ke mohopolo oa mahlale a khomphutha o bolelang nako eo algorithm e itseng kapa lenaneo le nkang ho e phetha, mabapi le boholo ba data e kentsoeng. E sebelisetsoa ho lekola le ho bapisa katleho ea li-algorithms, hammoho le ho tseba mathata a ka bang teng a tsebetso. Ha re sekaseka ho rarahana ha nako ea algorithm, hangata re leka ho hakanya palo ea tsebetso ea mantlha joalo ka litlatsetso, katiso, kapa likabelo eo e e etsang. Kaha likhomphieutha tse fapaneng li na le lebelo le fapaneng la ho sebetsa, ho rarahana ha nako ea algorithm hangata ho hlalosoa ho latela boholo ba tlhahiso ho e-na le ho latela nako ea sebele e nkiloeng ho phethahatsa. Ho lekanya ho rarahana ha nako, re sebelisa Big O notation, e hlalosang moeli o ka holimo oa sekhahla sa kholo ea nako ea ho sebetsa ea algorithm. Mohlala, haeba algorithm e nka nako ea On ho e etsa, ho bolela hore nako ea eona ea ho sebetsa e eketseha ho latela boholo ba tlhahiso. Mathata a mang a tloaelehileng a nako a kenyelletsa Olog n, On2, le O2n, tse tsamaellanang le sekhahla sa kholo ea logarithmic, quadratic, le exponential, ka ho latellana. Ka bokhutšoanyane, ho rarahana ha nako ke metric e sebetsang bakeng sa ho lekola tšebetso ea li-algorithms me e re thusa ho tseba hore na ke efe e loketseng mosebetsi o itseng. Ka ho hlahloba ho rarahana ha nako ea algorithm, re ka etsa bonnete ba hore mananeo a rona a na le linako tse amohelehang tsa ho sebetsa, esita le ha re sebetsana le lihlopha tse kholo tsa data. 