* This class provides methods for managing a queue of URLs to be crawled,
 * including adding, deleting, and retrieving URLs from the queue.
 * It uses a {@link MemoryDataHelper} to store the URL queue data in memory.
 *
 * <p>
 * The class is responsible for:
 * </p>
 * <ul>
 *   <li>Updating session IDs for URL queues.</li>
 *   <li>Adding new URLs to the queue.</li>

   
     * If you have above configuration and using _**Windows**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.
       * Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup).
     * If you have above configuration and using _**Ubuntu/Linux**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.

    @Test
    public void testAddInitialSettings() {
        // Test adding single setting
        SuggestSettingsBuilder result = builder.addInitialSettings("key1", "value1");
        assertEquals(builder, result); // Test method chaining
        assertEquals(1, builder.initialSettings.size());
        assertEquals("value1", builder.initialSettings.get("key1"));

        // Test adding multiple settings
        builder.addInitialSettings("key2", 100);

	ErrInvalidServiceS3: {
		Code:           "AuthorizationParametersError",
		Description:    "Error parsing the Credential/X-Amz-Credential parameter; incorrect service. This endpoint belongs to \"s3\".",
		HTTPStatusCode: http.StatusBadRequest,
	},
	ErrInvalidServiceSTS: {
		Code:           "AuthorizationParametersError",
		Description:    "Error parsing the Credential parameter; incorrect service. This endpoint belongs to \"sts\".",

2. Running the CI scripts locally, as explained below
3. **Google employees only**: Google employees can use an internal-only tool
called "MLCI" that makes testing more convenient: it can execute any full CI job
against a pending change. Search for "MLCI" internally to find it.

You may invoke a CI script of your choice by following these instructions:

```bash
cd tensorflow-git-dir

| `minio_cluster_ilm_expiry_pending_tasks`              | Number of pending ILM expiry tasks in the queue. <br><br>Type: gauge                              | `server` |
| `minio_cluster_ilm_transition_active_tasks`           | Number of active ILM transition tasks. <br><br>Type: gauge                                        | `server` |

	// in Queue stats for bucket - from qCache
	QStat InQueueMetric `json:"queued"`
	// Deprecated fields
	// Pending size in bytes
	PendingSize int64 `json:"pendingReplicationSize"`
	// Failed size in bytes
	FailedSize int64 `json:"failedReplicationSize"`
	// Total number of pending operations including metadata updates
	PendingCount int64 `json:"pendingReplicationCount"`

		rs:        ReplicationState{ReplicationStatusInternal: "arn1=PENDING;", Targets: map[string]replication.StatusType{"arn1": "PENDING"}},
		expStatus: replication.Pending,
	},
	{ // 3. replication status for one target - incorrect format
		name:      "replication status for one target",
		rs:        ReplicationState{ReplicationStatusInternal: "arn1=PENDING"},
		expStatus: replication.StatusType(""),
	},

}

// PendingStatus returns a stringified representation of internal replication status with all targets marked as `PENDING`
func (d ReplicateDecision) PendingStatus() string {
	b := new(bytes.Buffer)
	for _, k := range d.targetsMap {
		if k.Replicate {
			fmt.Fprintf(b, "%s=%s;", k.Arn, replication.Pending.String())
		}
	}
	return b.String()
}

In that case, they will be applied to all the *path operations* in the application:

{* ../../docs_src/dependencies/tutorial012_an_py39.py hl[16] *}


And all the ideas in the section about [adding `dependencies` to the *path operation decorators*](dependencies-in-path-operation-decorators.md){.internal-link target=_blank} still apply, but in this case, to all of the *path operations* in the app.