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`@@ -158,6 +158,7 @@ print('Time taken (memoized function using manual decorator):', time.time() - st`
`158`	`158`	```
`159`	`159`
`160`	`160`	`And here’s the output:`
	`161`	`+`
`161`	`162`	`![](images/output_normal_memoized.png)`
`162`	`163`
`163`	`164`	`Notice the time difference between the two functions. Both take almost`
`@@ -231,6 +232,7 @@ print('Time taken (memoized function with LRU cache):', time.time() - start_time`
`231`	`232`	```
`232`	`233`
`233`	`234`	`This produced the following output:`
	`235`	`+`
`234`	`236`	`![](images/output_normal_lru.png)`
`235`	`237`
`236`	`238`	`###Performance comparison`
`@@ -248,6 +250,7 @@ three functions for different numbers of requests to these functions:`
`248`	`250`	`As the number of requests to the functions increases, you can see a`
`249`	`251`	`significant reduction in execution times using the caching strategy. The`
`250`	`252`	`following comparison chart depicts these results:`
	`253`	`+`
`251`	`254`	`![](images/comparison-chart.png)`
`252`	`255`
`253`	`256`	`The comparison results clearly show that using a caching strategy in`

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