///

## Duplicate headers { #duplicate-headers }

It is possible to receive duplicate headers. That means, the same header with multiple values.

You can define those cases using a list in the type declaration.

You will receive all the values from the duplicate header as a Python `list`.

                    type.defaultBaselines,
                    display = ParameterDisplay.PROMPT,
                    allowEmpty = true,
                    description = "The baselines you want to run performance tests against. Empty means default baseline.",
                )
                param("env.PERFORMANCE_DB_PASSWORD_TCAGENT", "%performance.db.password.tcagent%")
                param("performance.db.username", "tcagent")

It is designed to be very simple to use, and to make it very easy for any developer to integrate other components with **FastAPI**.

## What is "Dependency Injection" { #what-is-dependency-injection }

**"Dependency Injection"** means, in programming, that there is a way for your code (in this case, your *path operation functions*) to declare things that it requires to work and use: "dependencies".

# o cursorSelectFetchSize: (NotRequired - Default null)
# o refreshMap: (NotRequired - Default map:{})
# o optimisticLockMap: (NotRequired - Default map:{})
#
# *The line that starts with '#' means comment-out.
#
map:{
    # /- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    # o isAvailableAddingSchemaToTableSqlName: (NotRequired - Default false)
    #   [true]

 * error codes, constants and text messages like "The device is not ready"
 * are provided.
 * <p>
 * The jCIFS client maps DOS error codes to NTSTATUS codes. This means that
 * the user may recieve a different error from a legacy server than that of
 * a newer varient such as Windows NT and above. If you should encounter
 * such a case, please report it to jcifs at samba dot org and we will

fi
# VERY basic check to ensure the [and-cuda] package variant is installable.
# Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is
# relevant. All of the GPU test machines have CUDA installed via other means,
# so I am not sure how to verify that the dependencies themselves are valid for
# the moment.
if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then
  echo "Checking to make sure tensorflow[and-cuda] is installable..."

	// Get the number of statement keys stored in the PreparedStmtDB.
	lens := len(conn.Stmts.Keys())
	// Check if the number of stored statement keys is 0.
	if lens == 0 {
		// If the number is 0, it means there are no statements stored in the LRU cache.
		// The test fails and an error message is output.
		t.Fatalf("lru should not be empty")
	}
	// Wait for 40 seconds to give the statements in the cache enough time to expire.

# o rdbDiXmlResourceName: (NotRequired - Default 'rdb.xml')
#
# {CSharp} Quill(CSharp Seasar) Only:
# o quillDataSourceName: (NotRequired - Default null)
#
# *The line that starts with '#' means comment-out.
#
map:{
    # ====================================================================================
    #                                                                               Spring

{* ../../docs_src/body_nested_models/tutorial009_py39.py hl[7] *}

/// tip

Keep in mind that JSON only supports `str` as keys.

But Pydantic has automatic data conversion.

This means that, even though your API clients can only send strings as keys, as long as those strings contain pure integers, Pydantic will convert them and validate them.

 2. It attempts to retrieve a connection with that address from the **connection pool**.
 3. If it doesn't find a connection in the pool, it selects a **route** to attempt. This usually means making a DNS request to get the server's IP addresses. It then selects a TLS version and proxy server if necessary.