* The builder allows setting parameters such as MIME type, filename, extractor name, maximum content length,
 * and cache file size to optimize the extraction process.
 *
 * <p>
 * The main purpose of this class is to simplify the extraction process by providing a fluent interface
 * for configuring the extraction parameters and handling the underlying complexities of content processing,

{* ../../docs_src/bigger_applications/app_an_py39/dependencies.py hl[3,6:8] title["app/dependencies.py"] *}

/// tip

We are using an invented header to simplify this example.

But in real cases you will get better results using the integrated [Security utilities](security/index.md){.internal-link target=_blank}.

///

  /**
   * These assertions ensure the [PublicSuffixDatabase] remains correct. The specification is
   * very flexible regarding wildcard rules, but this flexibility is not something currently used
   * in practice. To simplify the implementation, we've avoided implementing the flexible rules in
   * favor of supporting what's actually used in practice. That means if these assertions ever fail,

* ✅ Simplify tests for request_forms. PR [#13184](https://github.com/fastapi/fastapi/pull/13184) by [@alejsdev](https://github.com/alejsdev).
* ✅ Simplify tests for path_query_params. PR [#13181](https://github.com/fastapi/fastapi/pull/13181) by [@alejsdev](https://github.com/alejsdev).

func extractHostPort(hostAddr string) (string, string, error) {
	var addr, scheme string

	if hostAddr == "" {
		return "", "", errors.New("unable to process empty address")
	}

	// Simplify the work of url.Parse() and always send a url with
	if !strings.HasPrefix(hostAddr, "http://") && !strings.HasPrefix(hostAddr, "https://") {
		hostAddr = "//" + hostAddr
	}

// does various checks while doing so. `input_nodes` will contain the same
// information as input_tensors just in a different structure to make
// following processing easier. TODO(iga): Simplify this nested structure.
absl::Status ProcessInputs(
    const TF_Graph* fn_body, const char* fn_name, int ninputs,
    const TF_Output* inputs, std::vector<OutputTensor>* input_tensors,

        File lmhostsFile = tempDir.resolve("lmhosts_include").toFile();

        try (FileWriter writer = new FileWriter(lmhostsFile)) {
            // Write main content without include directive to simplify test
            writer.write("192.168.1.100 HOST1\n");
            writer.write("# Test comment\n");
            writer.write("192.168.1.101 HOST2\n");
        }

## Recap { #recap }

You can use Pydantic Settings to handle the settings or configurations for your application, with all the power of Pydantic models.

* By using a dependency you can simplify testing.
* You can use `.env` files with it.

func (c *LRU[K, V]) Cap() int {
	return c.size
}

// Entry is an LRU Entry
type Entry[K comparable, V any] struct {
	// Next and previous pointers in the doubly-linked list of elements.
	// To simplify the implementation, internally a list l is implemented
	// as a ring, such that &l.root is both the next element of the last
	// list element (l.Back()) and the previous element of the first list
	// element (l.Front()).

		sr := s[i]
		tr := t[i]
		if sr|tr >= utf8.RuneSelf {
			goto hasUnicode
		}

		// Easy case.
		if tr == sr {
			continue
		}

		// Make sr < tr to simplify what follows.
		if tr < sr {
			tr, sr = sr, tr
		}
		// ASCII only, sr/tr must be upper/lower case
		if 'A' <= sr && sr <= 'Z' && tr == sr+'a'-'A' {
			continue
		}
		return false
	}