style: apply ruff format to all files

mlflow_utils/cli.py

@@ -63,10 +63,7 @@ def cmd_list_experiments(args):
 
 def cmd_compare(args):
     """Compare recent runs in an experiment."""
-    analyzer = ExperimentAnalyzer(
-        args.experiment,
-        tracking_uri=args.tracking_uri
-    )
+    analyzer = ExperimentAnalyzer(args.experiment, tracking_uri=args.tracking_uri)
 
     if args.run_ids:
         run_ids = args.run_ids.split(",")
@@ -82,10 +79,7 @@ def cmd_compare(args):
 
 def cmd_best(args):
     """Find the best run by a metric."""
-    analyzer = ExperimentAnalyzer(
-        args.experiment,
-        tracking_uri=args.tracking_uri
-    )
+    analyzer = ExperimentAnalyzer(args.experiment, tracking_uri=args.tracking_uri)
 
     best_run = analyzer.get_best_run(
         metric=args.metric,
@@ -115,10 +109,7 @@ def cmd_best(args):
 
 def cmd_summary(args):
     """Get metrics summary for an experiment."""
-    analyzer = ExperimentAnalyzer(
-        args.experiment,
-        tracking_uri=args.tracking_uri
-    )
+    analyzer = ExperimentAnalyzer(args.experiment, tracking_uri=args.tracking_uri)
 
     summary = analyzer.get_metrics_summary(
         hours=args.hours,
@@ -201,10 +192,7 @@ def cmd_promote(args):
 
 def cmd_query(args):
     """Query runs with a filter."""
-    analyzer = ExperimentAnalyzer(
-        args.experiment,
-        tracking_uri=args.tracking_uri
-    )
+    analyzer = ExperimentAnalyzer(args.experiment, tracking_uri=args.tracking_uri)
 
     runs = analyzer.search_runs(
         filter_string=args.filter or "",

mlflow_utils/client.py

@@ -22,41 +22,24 @@ class MLflowConfig:
 
     # Tracking server URIs
     tracking_uri: str = field(
-        default_factory=lambda: os.environ.get(
-            "MLFLOW_TRACKING_URI",
-            "http://mlflow.mlflow.svc.cluster.local:80"
-        )
+        default_factory=lambda: os.environ.get("MLFLOW_TRACKING_URI", "http://mlflow.mlflow.svc.cluster.local:80")
     )
     external_uri: str = field(
-        default_factory=lambda: os.environ.get(
-            "MLFLOW_EXTERNAL_URI",
-            "https://mlflow.lab.daviestechlabs.io"
-        )
+        default_factory=lambda: os.environ.get("MLFLOW_EXTERNAL_URI", "https://mlflow.lab.daviestechlabs.io")
     )
 
     # Artifact storage (NFS PVC mount)
     artifact_location: str = field(
-        default_factory=lambda: os.environ.get(
-            "MLFLOW_ARTIFACT_LOCATION",
-            "/mlflow/artifacts"
-        )
+        default_factory=lambda: os.environ.get("MLFLOW_ARTIFACT_LOCATION", "/mlflow/artifacts")
     )
 
     # Default experiment settings
     default_experiment: str = field(
-        default_factory=lambda: os.environ.get(
-            "MLFLOW_DEFAULT_EXPERIMENT",
-            "llm-workflows"
-        )
+        default_factory=lambda: os.environ.get("MLFLOW_DEFAULT_EXPERIMENT", "llm-workflows")
     )
 
     # Service identification
-    service_name: str = field(
-        default_factory=lambda: os.environ.get(
-            "OTEL_SERVICE_NAME",
-            "unknown-service"
-        )
-    )
+    service_name: str = field(default_factory=lambda: os.environ.get("OTEL_SERVICE_NAME", "unknown-service"))
 
     # Additional tags to add to all runs
     default_tags: Dict[str, str] = field(default_factory=dict)
@@ -85,10 +68,7 @@ def get_tracking_uri(external: bool = False) -> str:
     return config.external_uri if external else config.tracking_uri
 
 
-def get_mlflow_client(
-    tracking_uri: Optional[str] = None,
-    configure_global: bool = True
-) -> MlflowClient:
+def get_mlflow_client(tracking_uri: Optional[str] = None, configure_global: bool = True) -> MlflowClient:
     """
     Get a configured MLflow client.
 
@@ -110,9 +90,7 @@ def get_mlflow_client(
 
 
 def ensure_experiment(
-    experiment_name: str,
-    artifact_location: Optional[str] = None,
-    tags: Optional[Dict[str, str]] = None
+    experiment_name: str, artifact_location: Optional[str] = None, tags: Optional[Dict[str, str]] = None
 ) -> str:
     """
     Ensure an experiment exists, creating it if necessary.
@@ -134,11 +112,7 @@ def ensure_experiment(
     if experiment is None:
         # Create the experiment
         artifact_loc = artifact_location or f"{config.artifact_location}/{experiment_name}"
-        experiment_id = client.create_experiment(
-            name=experiment_name,
-            artifact_location=artifact_loc,
-            tags=tags or {}
-        )
+        experiment_id = client.create_experiment(name=experiment_name, artifact_location=artifact_loc, tags=tags or {})
         logger.info(f"Created experiment '{experiment_name}' with ID: {experiment_id}")
     else:
         experiment_id = experiment.experiment_id
@@ -148,9 +122,7 @@ def ensure_experiment(
 
 
 def get_or_create_registered_model(
-    model_name: str,
-    description: Optional[str] = None,
-    tags: Optional[Dict[str, str]] = None
+    model_name: str, description: Optional[str] = None, tags: Optional[Dict[str, str]] = None
 ) -> str:
     """
     Get or create a registered model in the Model Registry.
@@ -172,9 +144,7 @@ def get_or_create_registered_model(
     except mlflow.exceptions.MlflowException:
         # Create the model
         client.create_registered_model(
-            name=model_name,
-            description=description or f"Model for {model_name}",
-            tags=tags or {}
+            name=model_name, description=description or f"Model for {model_name}", tags=tags or {}
         )
         logger.info(f"Created registered model: {model_name}")
 

mlflow_utils/experiment_comparison.py

@@ -51,6 +51,7 @@ logger = logging.getLogger(__name__)
 @dataclass
 class RunComparison:
     """Comparison result for multiple MLflow runs."""
+
     run_ids: List[str]
     experiment_name: str
 
@@ -111,6 +112,7 @@ class RunComparison:
 @dataclass
 class PromotionRecommendation:
     """Recommendation for model promotion."""
+
     model_name: str
     version: Optional[int]
     recommended: bool
@@ -262,13 +264,9 @@ class ExperimentAnalyzer:
 
             # Metadata
             comparison.run_names[run_id] = run.info.run_name or run_id[:8]
-            comparison.start_times[run_id] = datetime.fromtimestamp(
-                run.info.start_time / 1000
-            )
+            comparison.start_times[run_id] = datetime.fromtimestamp(run.info.start_time / 1000)
             if run.info.end_time:
-                comparison.durations[run_id] = (
-                    run.info.end_time - run.info.start_time
-                ) / 1000
+                comparison.durations[run_id] = (run.info.end_time - run.info.start_time) / 1000
 
             # Metrics
             for key, value in run.data.metrics.items():
@@ -288,10 +286,7 @@ class ExperimentAnalyzer:
                 continue
 
             # Determine if lower is better based on metric name
-            minimize = any(
-                term in metric_name.lower()
-                for term in ["latency", "error", "loss", "time"]
-            )
+            minimize = any(term in metric_name.lower() for term in ["latency", "error", "loss", "time"])
 
             if minimize:
                 best_id = min(values.keys(), key=lambda k: values[k])
@@ -330,10 +325,7 @@ class ExperimentAnalyzer:
         )
 
         # Filter to only runs that have the metric
-        runs_with_metric = [
-            r for r in runs
-            if metric in r.data.metrics
-        ]
+        runs_with_metric = [r for r in runs if metric in r.data.metrics]
 
         return runs_with_metric[0] if runs_with_metric else None
 

mlflow_utils/inference_tracker.py

@@ -29,6 +29,7 @@ logger = logging.getLogger(__name__)
 @dataclass
 class InferenceMetrics:
     """Metrics collected during an inference request."""
+
     request_id: str
     user_id: Optional[str] = None
     session_id: Optional[str] = None
@@ -190,31 +191,22 @@ class InferenceMetricsTracker:
 
         # Initialize MLflow in thread pool to avoid blocking
         loop = asyncio.get_event_loop()
-        await loop.run_in_executor(
-            self._executor,
-            self._init_mlflow
-        )
+        await loop.run_in_executor(self._executor, self._init_mlflow)
 
         if self.enable_batching:
             self._flush_task = asyncio.create_task(self._periodic_flush())
 
-        logger.info(
-            f"InferenceMetricsTracker started for {self.service_name} "
-            f"(experiment: {self.experiment_name})"
-        )
+        logger.info(f"InferenceMetricsTracker started for {self.service_name} (experiment: {self.experiment_name})")
 
     def _init_mlflow(self) -> None:
         """Initialize MLflow client and experiment (runs in thread pool)."""
-        self._client = get_mlflow_client(
-            tracking_uri=self.tracking_uri,
-            configure_global=True
-        )
+        self._client = get_mlflow_client(tracking_uri=self.tracking_uri, configure_global=True)
         self._experiment_id = ensure_experiment(
             self.experiment_name,
             tags={
                 "service": self.service_name,
                 "type": "inference-metrics",
-            }
+            },
         )
 
     async def stop(self) -> None:
@@ -265,10 +257,7 @@ class InferenceMetricsTracker:
         else:
             # Immediate logging in thread pool
             loop = asyncio.get_event_loop()
-            await loop.run_in_executor(
-                self._executor,
-                partial(self._log_single_inference, metrics)
-            )
+            await loop.run_in_executor(self._executor, partial(self._log_single_inference, metrics))
 
     async def _periodic_flush(self) -> None:
         """Periodically flush batched metrics."""
@@ -287,10 +276,7 @@ class InferenceMetricsTracker:
 
         # Log in thread pool
         loop = asyncio.get_event_loop()
-        await loop.run_in_executor(
-            self._executor,
-            partial(self._log_batch, batch)
-        )
+        await loop.run_in_executor(self._executor, partial(self._log_batch, batch))
 
     def _log_single_inference(self, metrics: InferenceMetrics) -> None:
         """Log a single inference request to MLflow (runs in thread pool)."""
@@ -302,7 +288,7 @@ class InferenceMetricsTracker:
                     "service": self.service_name,
                     "request_id": metrics.request_id,
                     "type": "single-inference",
-                }
+                },
             ):
                 mlflow.log_params(metrics.as_params_dict())
                 mlflow.log_metrics(metrics.as_metrics_dict())
@@ -336,7 +322,7 @@ class InferenceMetricsTracker:
                     "service": self.service_name,
                     "type": "batch-inference",
                     "batch_size": str(len(batch)),
-                }
+                },
             ):
                 # Log aggregate metrics
                 mlflow.log_metrics(aggregates)
@@ -352,12 +338,14 @@ class InferenceMetricsTracker:
                 premium_count = sum(1 for m in batch if m.is_premium)
                 error_count = sum(1 for m in batch if m.has_error)
 
-                mlflow.log_metrics({
+                mlflow.log_metrics(
+                    {
                         "rag_enabled_pct": rag_enabled_count / len(batch) * 100,
                         "streaming_pct": streaming_count / len(batch) * 100,
                         "premium_pct": premium_count / len(batch) * 100,
                         "error_rate": error_count / len(batch) * 100,
-                })
+                    }
+                )
 
                 # Log model distribution
                 model_counts: Dict[str, int] = defaultdict(int)
@@ -366,20 +354,14 @@ class InferenceMetricsTracker:
                         model_counts[m.model_name] += 1
 
                 if model_counts:
-                    mlflow.log_dict(
-                        {"models": dict(model_counts)},
-                        "model_distribution.json"
-                    )
+                    mlflow.log_dict({"models": dict(model_counts)}, "model_distribution.json")
 
             logger.info(f"Logged batch of {len(batch)} inference metrics")
 
         except Exception as e:
             logger.error(f"Failed to log batch metrics: {e}")
 
-    def _calculate_aggregates(
-        self,
-        batch: List[InferenceMetrics]
-    ) -> Dict[str, float]:
+    def _calculate_aggregates(self, batch: List[InferenceMetrics]) -> Dict[str, float]:
         """Calculate aggregate statistics from a batch of metrics."""
         import statistics
 

mlflow_utils/kfp_components.py

@@ -47,17 +47,14 @@ MLFLOW_PACKAGES = [
 ]
 
 
-@dsl.component(
-    base_image=MLFLOW_IMAGE,
-    packages_to_install=MLFLOW_PACKAGES
-)
+@dsl.component(base_image=MLFLOW_IMAGE, packages_to_install=MLFLOW_PACKAGES)
 def create_mlflow_run(
     experiment_name: str,
     run_name: str,
     mlflow_tracking_uri: str = "http://mlflow.mlflow.svc.cluster.local:80",
     tags: Dict[str, str] = None,
     params: Dict[str, str] = None,
-) -> NamedTuple('RunInfo', [('run_id', str), ('experiment_id', str), ('artifact_uri', str)]):
+) -> NamedTuple("RunInfo", [("run_id", str), ("experiment_id", str), ("artifact_uri", str)]):
     """
     Create a new MLflow run for the pipeline.
 
@@ -90,8 +87,7 @@ def create_mlflow_run(
     experiment = client.get_experiment_by_name(experiment_name)
     if experiment is None:
         experiment_id = client.create_experiment(
-            name=experiment_name,
-            artifact_location=f"/mlflow/artifacts/{experiment_name}"
+            name=experiment_name, artifact_location=f"/mlflow/artifacts/{experiment_name}"
         )
     else:
         experiment_id = experiment.experiment_id
@@ -122,14 +118,11 @@ def create_mlflow_run(
     # End run (KFP components are isolated, we'll resume in other components)
     mlflow.end_run()
 
-    RunInfo = namedtuple('RunInfo', ['run_id', 'experiment_id', 'artifact_uri'])
+    RunInfo = namedtuple("RunInfo", ["run_id", "experiment_id", "artifact_uri"])
     return RunInfo(run_id, experiment_id, artifact_uri)
 
 
-@dsl.component(
-    base_image=MLFLOW_IMAGE,
-    packages_to_install=MLFLOW_PACKAGES
-)
+@dsl.component(base_image=MLFLOW_IMAGE, packages_to_install=MLFLOW_PACKAGES)
 def log_params_component(
     run_id: str,
     params: Dict[str, str],
@@ -158,10 +151,7 @@ def log_params_component(
     return run_id
 
 
-@dsl.component(
-    base_image=MLFLOW_IMAGE,
-    packages_to_install=MLFLOW_PACKAGES
-)
+@dsl.component(base_image=MLFLOW_IMAGE, packages_to_install=MLFLOW_PACKAGES)
 def log_metrics_component(
     run_id: str,
     metrics: Dict[str, float],
@@ -192,10 +182,7 @@ def log_metrics_component(
     return run_id
 
 
-@dsl.component(
-    base_image=MLFLOW_IMAGE,
-    packages_to_install=MLFLOW_PACKAGES
-)
+@dsl.component(base_image=MLFLOW_IMAGE, packages_to_install=MLFLOW_PACKAGES)
 def log_artifact_component(
     run_id: str,
     artifact_path: str,
@@ -225,10 +212,7 @@ def log_artifact_component(
     return run_id
 
 
-@dsl.component(
-    base_image=MLFLOW_IMAGE,
-    packages_to_install=MLFLOW_PACKAGES
-)
+@dsl.component(base_image=MLFLOW_IMAGE, packages_to_install=MLFLOW_PACKAGES)
 def log_dict_artifact(
     run_id: str,
     data: Dict[str, Any],
@@ -258,23 +242,20 @@ def log_dict_artifact(
     client = MlflowClient()
 
     # Ensure .json extension
-    if not filename.endswith('.json'):
-        filename += '.json'
+    if not filename.endswith(".json"):
+        filename += ".json"
 
     # Write to temp file and log
     with tempfile.TemporaryDirectory() as tmpdir:
         filepath = Path(tmpdir) / filename
-        with open(filepath, 'w') as f:
+        with open(filepath, "w") as f:
             json.dump(data, f, indent=2)
         client.log_artifact(run_id, str(filepath))
 
     return run_id
 
 
-@dsl.component(
-    base_image=MLFLOW_IMAGE,
-    packages_to_install=MLFLOW_PACKAGES
-)
+@dsl.component(base_image=MLFLOW_IMAGE, packages_to_install=MLFLOW_PACKAGES)
 def end_mlflow_run(
     run_id: str,
     status: str = "FINISHED",
@@ -310,10 +291,7 @@ def end_mlflow_run(
     return run_id
 
 
-@dsl.component(
-    base_image=MLFLOW_IMAGE,
-    packages_to_install=MLFLOW_PACKAGES + ["httpx"]
-)
+@dsl.component(base_image=MLFLOW_IMAGE, packages_to_install=MLFLOW_PACKAGES + ["httpx"])
 def log_training_metrics(
     run_id: str,
     model_type: str,
@@ -370,7 +348,7 @@ def log_training_metrics(
     # Log full config as artifact
     with tempfile.TemporaryDirectory() as tmpdir:
         config_path = Path(tmpdir) / "training_config.json"
-        with open(config_path, 'w') as f:
+        with open(config_path, "w") as f:
             json.dump(training_config, f, indent=2)
         client.log_artifact(run_id, str(config_path))
 
@@ -382,10 +360,7 @@ def log_training_metrics(
     return run_id
 
 
-@dsl.component(
-    base_image=MLFLOW_IMAGE,
-    packages_to_install=MLFLOW_PACKAGES
-)
+@dsl.component(base_image=MLFLOW_IMAGE, packages_to_install=MLFLOW_PACKAGES)
 def log_document_ingestion_metrics(
     run_id: str,
     source_url: str,
@@ -452,10 +427,7 @@ def log_document_ingestion_metrics(
     return run_id
 
 
-@dsl.component(
-    base_image=MLFLOW_IMAGE,
-    packages_to_install=MLFLOW_PACKAGES
-)
+@dsl.component(base_image=MLFLOW_IMAGE, packages_to_install=MLFLOW_PACKAGES)
 def log_evaluation_results(
     run_id: str,
     model_name: str,
@@ -502,7 +474,7 @@ def log_evaluation_results(
     if sample_results:
         with tempfile.TemporaryDirectory() as tmpdir:
             results_path = Path(tmpdir) / "evaluation_results.json"
-            with open(results_path, 'w') as f:
+            with open(results_path, "w") as f:
                 json.dump(sample_results, f, indent=2)
             client.log_artifact(run_id, str(results_path))
 

mlflow_utils/model_registry.py

@@ -192,7 +192,7 @@ def register_model_for_kserve(
             tags={
                 "model.type": model_type,
                 "deployment.target": "kserve",
-            }
+            },
         )
         logger.info(f"Created registered model: {model_name}")
 
@@ -206,13 +206,10 @@ def register_model_for_kserve(
             **(tags or {}),
             "model.type": model_type,
             **kserve_config.as_dict(),
-        }
+        },
     )
 
-    logger.info(
-        f"Registered model version {model_version.version} "
-        f"for {model_name} (type: {model_type})"
-    )
+    logger.info(f"Registered model version {model_version.version} for {model_name} (type: {model_type})")
 
     return model_version
 
@@ -412,7 +409,8 @@ def generate_kserve_manifest(
 
     if config.container_image:
         # Custom container
-        predictor["containers"] = [{
+        predictor["containers"] = [
+            {
                 "name": "predictor",
                 "image": config.container_image,
                 "ports": [{"containerPort": config.container_port, "protocol": "TCP"}],
@@ -426,11 +424,9 @@ def generate_kserve_manifest(
                         "memory": config.memory_limit,
                     },
                 },
-            "env": [
-                {"name": k, "value": v}
-                for k, v in config.env_vars.items()
-            ],
-        }]
+                "env": [{"name": k, "value": v} for k, v in config.env_vars.items()],
+            }
+        ]
 
         # Add GPU if needed
         if config.gpu_count > 0:
@@ -494,7 +490,7 @@ def generate_kserve_yaml(
     yaml_str = yaml.dump(manifest, default_flow_style=False, sort_keys=False)
 
     if output_path:
-        with open(output_path, 'w') as f:
+        with open(output_path, "w") as f:
             f.write(yaml_str)
         logger.info(f"Wrote KServe manifest to {output_path}")
 

mlflow_utils/tracker.py

@@ -23,6 +23,7 @@ logger = logging.getLogger(__name__)
 @dataclass
 class PipelineMetadata:
     """Metadata about the Kubeflow Pipeline run."""
+
     pipeline_name: str
     run_id: str
     run_name: Optional[str] = None
@@ -30,12 +31,8 @@ class PipelineMetadata:
     namespace: str = "ai-ml"
 
     # KFP-specific metadata (populated from environment if available)
-    kfp_run_id: Optional[str] = field(
-        default_factory=lambda: os.environ.get("KFP_RUN_ID")
-    )
-    kfp_pod_name: Optional[str] = field(
-        default_factory=lambda: os.environ.get("KFP_POD_NAME")
-    )
+    kfp_run_id: Optional[str] = field(default_factory=lambda: os.environ.get("KFP_RUN_ID"))
+    kfp_pod_name: Optional[str] = field(default_factory=lambda: os.environ.get("KFP_POD_NAME"))
 
     def as_tags(self) -> Dict[str, str]:
         """Convert metadata to MLflow tags."""
@@ -142,10 +139,7 @@ class MLflowTracker:
         Yields:
             The MLflow run object
         """
-        self.client = get_mlflow_client(
-            tracking_uri=self.tracking_uri,
-            configure_global=True
-        )
+        self.client = get_mlflow_client(tracking_uri=self.tracking_uri, configure_global=True)
 
         # Ensure experiment exists
         experiment_id = ensure_experiment(self.experiment_name)
@@ -163,8 +157,7 @@ class MLflowTracker:
             self.run_id = self.run.info.run_id
 
             logger.info(
-                f"Started MLflow run '{self.run_name}' "
-                f"(ID: {self.run_id}) in experiment '{self.experiment_name}'"
+                f"Started MLflow run '{self.run_name}' (ID: {self.run_id}) in experiment '{self.experiment_name}'"
             )
 
             yield self.run
@@ -214,11 +207,7 @@ class MLflowTracker:
         """Log a single parameter."""
         self.log_params({key: value})
 
-    def log_metrics(
-        self,
-        metrics: Dict[str, Union[float, int]],
-        step: Optional[int] = None
-    ) -> None:
+    def log_metrics(self, metrics: Dict[str, Union[float, int]], step: Optional[int] = None) -> None:
         """
         Log metrics to the current run.
 
@@ -233,20 +222,11 @@ class MLflowTracker:
         mlflow.log_metrics(metrics, step=step)
         logger.debug(f"Logged {len(metrics)} metrics")
 
-    def log_metric(
-        self,
-        key: str,
-        value: Union[float, int],
-        step: Optional[int] = None
-    ) -> None:
+    def log_metric(self, key: str, value: Union[float, int], step: Optional[int] = None) -> None:
         """Log a single metric."""
         self.log_metrics({key: value}, step=step)
 
-    def log_artifact(
-        self,
-        local_path: str,
-        artifact_path: Optional[str] = None
-    ) -> None:
+    def log_artifact(self, local_path: str, artifact_path: Optional[str] = None) -> None:
         """
         Log an artifact file to the current run.
 
@@ -261,11 +241,7 @@ class MLflowTracker:
         mlflow.log_artifact(local_path, artifact_path)
         logger.info(f"Logged artifact: {local_path}")
 
-    def log_artifacts(
-        self,
-        local_dir: str,
-        artifact_path: Optional[str] = None
-    ) -> None:
+    def log_artifacts(self, local_dir: str, artifact_path: Optional[str] = None) -> None:
         """
         Log all files in a directory as artifacts.
 
@@ -280,12 +256,7 @@ class MLflowTracker:
         mlflow.log_artifacts(local_dir, artifact_path)
         logger.info(f"Logged artifacts from: {local_dir}")
 
-    def log_dict(
-        self,
-        data: Dict[str, Any],
-        filename: str,
-        artifact_path: Optional[str] = None
-    ) -> None:
+    def log_dict(self, data: Dict[str, Any], filename: str, artifact_path: Optional[str] = None) -> None:
         """
         Log a dictionary as a JSON artifact.
 
@@ -311,7 +282,7 @@ class MLflowTracker:
         model_name: str,
         model_path: Optional[str] = None,
         framework: str = "pytorch",
-        extra_info: Optional[Dict[str, Any]] = None
+        extra_info: Optional[Dict[str, Any]] = None,
     ) -> None:
         """
         Log model information as parameters and tags.
@@ -341,11 +312,7 @@ class MLflowTracker:
         mlflow.set_tag("model.name", model_name)
 
     def log_dataset_info(
-        self,
-        name: str,
-        source: str,
-        size: Optional[int] = None,
-        extra_info: Optional[Dict[str, Any]] = None
+        self, name: str, source: str, size: Optional[int] = None, extra_info: Optional[Dict[str, Any]] = None
     ) -> None:
         """
         Log dataset information.