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Over the past decade, neural networks have succeeded immensely in various industries. However, the black-box nature of their predictions has prevented their broader and more reliable adoption in fields such as health and security. This has led researchers to investigate ways to explain neural network decisions.
One approach to explaining neural network decisions is through saliency maps, which highlight areas of the input that a neural network uses most while making a prediction. However, these methods often produce noisy results that do not clearly understand the decisions made.
With cy.intercept(), you can intercept HTTP requests and responses in your tests, and perform actions like modifying the response, delaying the response, or returning a custom response.
When a request is intercepted by cy.intercept() the request is prevented from being sent to the server, and instead, Cypress will respond with the mock data you provide. This allows you to test different scenarios and responses from a server without actually having to make requests to it.