prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [BUG] uc_volume_dataset_source only validates file paths, not folder paths
  ### Issues Policy acknowledgement
  
  - [X] I have read and agree to submit bug reports in accordance with the [issues policy](https://www.github.com/mlflow/mlflow/blob/master/ISSUE_POLICY.md)
  
  ### Where did you encounter this bug?
  
  Local machine
  
  ### Willingness to contribute
  
  Yes. I would be willing to contribute a fix for this bug with guidance from the MLflow community.
  
  ### MLflow version
  
  mlflow-2.12.2
  
  ### System information
  
  - **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:
  - **Python version**:
  - **yarn version, if running the dev UI**:
  
  
  ### Describe the problem
  
  https://github.com/mlflow/mlflow/blob/72df4a2a0f44c52179dfbdc7d47ad10f58ceec39/mlflow/data/uc_volume_dataset_source.py#L28 doesn't verify folder paths, only file paths
  
  ### Tracking information
  
  <!-- PLEASE KEEP BACKTICKS AND CHECK PREVIEW -->
  ```shell
  REPLACE_ME
  ```
  
  
  ### Code to reproduce issue
  
  <!-- PLEASE KEEP BACKTICKS AND CHECK PREVIEW -->
  ```
  REPLACE_ME
  ```
  
  
  ### Stack trace
  
  <!-- PLEASE KEEP BACKTICKS AND CHECK PREVIEW -->
  ```
  REPLACE_ME
  ```
  
  
  ### Other info / logs
  
  <!-- PLEASE KEEP BACKTICKS AND CHECK PREVIEW -->
  ```
  REPLACE_ME
  ```
  
  
  ### What component(s) does this bug affect?
  
  - [ ] `area/artifacts`: Artifact stores and artifact logging
  - [ ] `area/build`: Build and test infrastructure for MLflow
  - [ ] `area/deployments`: MLflow Deployments client APIs, server, and third-party Deployments integrations
  - [ ] `area/docs`: MLflow documentation pages
  - [ ] `area/examples`: Example code
  - [ ] `area/model-registry`: Model Registry service, APIs, and the fluent client calls for Model Registry
  - [ ] `area/models`: MLmodel format, model serialization/deserialization, flavors
  - [ ] `area/recipes`: Recipes, Recipe APIs, Recipe configs, Recipe Templates
  - [ ] `area/projects`: MLproject format, project running backends
  - [ ] `area/scoring`: MLflow Model server, model deployment tools, Spark UDFs
  - [ ] `area/server-infra`: MLflow Tracking server backend
  - [ ] `area/tracking`: Tracking Service, tracking client APIs, autologging
  
  ### What interface(s) does this bug affect?
  
  - [ ] `area/uiux`: Front-end, user experience, plotting, JavaScript, JavaScript dev server
  - [ ] `area/docker`: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
  - [ ] `area/sqlalchemy`: Use of SQLAlchemy in the Tracking Service or Model Registry
  - [ ] `area/windows`: Windows support
  
  ### What language(s) does this bug affect?
  
  - [ ] `language/r`: R APIs and clients
  - [ ] `language/java`: Java APIs and clients
  - [ ] `language/new`: Proposals for new client languages
  
  ### What integration(s) does this bug affect?
  
  - [ ] `integrations/azure`: Azure and Azure ML integrations
  - [ ] `integrations/sagemaker`: SageMaker integrations
  - [ ] `integrations/databricks`: Databricks integrations
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `mlflow/data/uc_volume_dataset_source.py`
  Content:
  ```
  1 import logging
  2 from typing import Any, Dict
  3 
  4 from mlflow.data.dataset_source import DatasetSource
  5 from mlflow.exceptions import MlflowException
  6 
  7 _logger = logging.getLogger(__name__)
  8 
  9 
  10 class UCVolumeDatasetSource(DatasetSource):
  11     """Represents the source of a dataset stored in Databricks Unified Catalog Volume.
  12 
  13     If you are using a delta table, please use `mlflow.data.delta_dataset_source.DeltaDatasetSource`
  14     instead. This `UCVolumeDatasetSource` does not provide loading function, and is mostly useful
  15     when you are logging a `mlflow.data.meta_dataset.MetaDataset` to MLflow, i.e., you want
  16     to log the source of dataset to MLflow without loading the dataset.
  17 
  18     Args:
  19         path: the UC path of your data. It should be a valid UC path following the pattern
  20             "/Volumes/{catalog}/{schema}/{volume}/{file_path}". For example,
  21             "/Volumes/MyCatalog/MySchema/MyVolume/MyFile.json".
  22     """
  23 
  24     def __init__(self, path: str):
  25         self._verify_uc_path_is_valid(path)
  26         self.path = path
  27 
  28     def _verify_uc_path_is_valid(self, path):
  29         """Verify if the path exists in Databricks Unified Catalog."""
  30         try:
  31             from databricks.sdk import WorkspaceClient
  32 
  33             w = WorkspaceClient()
  34         except ImportError:
  35             _logger.warning(
  36                 "Cannot verify the path of `UCVolumeDatasetSource` because of missing"
  37                 "`databricks-sdk`. Please install `databricks-sdk` via "
  38                 "`pip install -U databricks-sdk`. This does not block creating "
  39                 "`UCVolumeDatasetSource`, but your `UCVolumeDatasetSource` might be invalid."
  40             )
  41             return
  42         except Exception:
  43             _logger.warning(
  44                 "Cannot verify the path of `UCVolumeDatasetSource` due to a connection failure "
  45                 "with Databricks workspace. Please run `mlflow.login()` to log in to Databricks. "
  46                 "This does not block creating `UCVolumeDatasetSource`, but your "
  47                 "`UCVolumeDatasetSource` might be invalid."
  48             )
  49             return
  50 
  51         try:
  52             w.files.get_metadata(path)
  53         except Exception:
  54             raise MlflowException(f"{path} does not exist in Databricks Unified Catalog.")
  55 
  56     @staticmethod
  57     def _get_source_type() -> str:
  58         return "uc_volume"
  59 
  60     @staticmethod
  61     def _can_resolve(raw_source: Any):
  62         raise NotImplementedError
  63 
  64     @classmethod
  65     def _resolve(cls, raw_source: str):
  66         raise NotImplementedError
  67 
  68     def to_dict(self) -> Dict[Any, Any]:
  69         return {"path": self.path}
  70 
  71     @classmethod
  72     def from_dict(cls, source_dict: Dict[Any, Any]) -> "UCVolumeDatasetSource":
  73         return cls(**source_dict)
  74
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/mlflow/data/uc_volume_dataset_source.py b/mlflow/data/uc_volume_dataset_source.py
  --- a/mlflow/data/uc_volume_dataset_source.py
  +++ b/mlflow/data/uc_volume_dataset_source.py
  @@ -22,10 +22,10 @@
       """
   
       def __init__(self, path: str):
  -        self._verify_uc_path_is_valid(path)
           self.path = path
  +        self._verify_uc_path_is_valid()
   
  -    def _verify_uc_path_is_valid(self, path):
  +    def _verify_uc_path_is_valid(self):
           """Verify if the path exists in Databricks Unified Catalog."""
           try:
               from databricks.sdk import WorkspaceClient
  @@ -49,9 +49,17 @@
               return
   
           try:
  -            w.files.get_metadata(path)
  +            # Check if `self.path` points to a valid UC file.
  +            w.files.get_metadata(self.path)
           except Exception:
  -            raise MlflowException(f"{path} does not exist in Databricks Unified Catalog.")
  +            try:
  +                # Check if `self.path` points to a valid UC directory.
  +                w.files.get_directory_metadata(self.path)
  +                # Append a slash to `self.path` to indicate it's a directory.
  +                self.path += "/" if not self.path.endswith("/") else ""
  +            except Exception:
  +                # Neither file nor directory exists, we throw an exception.
  +                raise MlflowException(f"{self.path} does not exist in Databricks Unified Catalog.")
   
       @staticmethod
       def _get_source_type() -> str:
  
verification_info: {"golden_diff": "diff --git a/mlflow/data/uc_volume_dataset_source.py b/mlflow/data/uc_volume_dataset_source.py\n--- a/mlflow/data/uc_volume_dataset_source.py\n+++ b/mlflow/data/uc_volume_dataset_source.py\n@@ -22,10 +22,10 @@\n     \"\"\"\n \n     def __init__(self, path: str):\n-        self._verify_uc_path_is_valid(path)\n         self.path = path\n+        self._verify_uc_path_is_valid()\n \n-    def _verify_uc_path_is_valid(self, path):\n+    def _verify_uc_path_is_valid(self):\n         \"\"\"Verify if the path exists in Databricks Unified Catalog.\"\"\"\n         try:\n             from databricks.sdk import WorkspaceClient\n@@ -49,9 +49,17 @@\n             return\n \n         try:\n-            w.files.get_metadata(path)\n+            # Check if `self.path` points to a valid UC file.\n+            w.files.get_metadata(self.path)\n         except Exception:\n-            raise MlflowException(f\"{path} does not exist in Databricks Unified Catalog.\")\n+            try:\n+                # Check if `self.path` points to a valid UC directory.\n+                w.files.get_directory_metadata(self.path)\n+                # Append a slash to `self.path` to indicate it's a directory.\n+                self.path += \"/\" if not self.path.endswith(\"/\") else \"\"\n+            except Exception:\n+                # Neither file nor directory exists, we throw an exception.\n+                raise MlflowException(f\"{self.path} does not exist in Databricks Unified Catalog.\")\n \n     @staticmethod\n     def _get_source_type() -> str:\n", "issue": "[BUG] uc_volume_dataset_source only validates file paths, not folder paths\n### Issues Policy acknowledgement\n\n- [X] I have read and agree to submit bug reports in accordance with the [issues policy](https://www.github.com/mlflow/mlflow/blob/master/ISSUE_POLICY.md)\n\n### Where did you encounter this bug?\n\nLocal machine\n\n### Willingness to contribute\n\nYes. I would be willing to contribute a fix for this bug with guidance from the MLflow community.\n\n### MLflow version\n\nmlflow-2.12.2\n\n### System information\n\n- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:\r\n- **Python version**:\r\n- **yarn version, if running the dev UI**:\r\n\n\n### Describe the problem\n\nhttps://github.com/mlflow/mlflow/blob/72df4a2a0f44c52179dfbdc7d47ad10f58ceec39/mlflow/data/uc_volume_dataset_source.py#L28 doesn't verify folder paths, only file paths\n\n### Tracking information\n\n<!-- PLEASE KEEP BACKTICKS AND CHECK PREVIEW -->\r\n```shell\r\nREPLACE_ME\r\n```\r\n\n\n### Code to reproduce issue\n\n<!-- PLEASE KEEP BACKTICKS AND CHECK PREVIEW -->\r\n```\r\nREPLACE_ME\r\n```\r\n\n\n### Stack trace\n\n<!-- PLEASE KEEP BACKTICKS AND CHECK PREVIEW -->\r\n```\r\nREPLACE_ME\r\n```\r\n\n\n### Other info / logs\n\n<!-- PLEASE KEEP BACKTICKS AND CHECK PREVIEW -->\r\n```\r\nREPLACE_ME\r\n```\r\n\n\n### What component(s) does this bug affect?\n\n- [ ] `area/artifacts`: Artifact stores and artifact logging\n- [ ] `area/build`: Build and test infrastructure for MLflow\n- [ ] `area/deployments`: MLflow Deployments client APIs, server, and third-party Deployments integrations\n- [ ] `area/docs`: MLflow documentation pages\n- [ ] `area/examples`: Example code\n- [ ] `area/model-registry`: Model Registry service, APIs, and the fluent client calls for Model Registry\n- [ ] `area/models`: MLmodel format, model serialization/deserialization, flavors\n- [ ] `area/recipes`: Recipes, Recipe APIs, Recipe configs, Recipe Templates\n- [ ] `area/projects`: MLproject format, project running backends\n- [ ] `area/scoring`: MLflow Model server, model deployment tools, Spark UDFs\n- [ ] `area/server-infra`: MLflow Tracking server backend\n- [ ] `area/tracking`: Tracking Service, tracking client APIs, autologging\n\n### What interface(s) does this bug affect?\n\n- [ ] `area/uiux`: Front-end, user experience, plotting, JavaScript, JavaScript dev server\n- [ ] `area/docker`: Docker use across MLflow's components, such as MLflow Projects and MLflow Models\n- [ ] `area/sqlalchemy`: Use of SQLAlchemy in the Tracking Service or Model Registry\n- [ ] `area/windows`: Windows support\n\n### What language(s) does this bug affect?\n\n- [ ] `language/r`: R APIs and clients\n- [ ] `language/java`: Java APIs and clients\n- [ ] `language/new`: Proposals for new client languages\n\n### What integration(s) does this bug affect?\n\n- [ ] `integrations/azure`: Azure and Azure ML integrations\n- [ ] `integrations/sagemaker`: SageMaker integrations\n- [ ] `integrations/databricks`: Databricks integrations\n", "before_files": [{"content": "import logging\nfrom typing import Any, Dict\n\nfrom mlflow.data.dataset_source import DatasetSource\nfrom mlflow.exceptions import MlflowException\n\n_logger = logging.getLogger(__name__)\n\n\nclass UCVolumeDatasetSource(DatasetSource):\n    \"\"\"Represents the source of a dataset stored in Databricks Unified Catalog Volume.\n\n    If you are using a delta table, please use `mlflow.data.delta_dataset_source.DeltaDatasetSource`\n    instead. This `UCVolumeDatasetSource` does not provide loading function, and is mostly useful\n    when you are logging a `mlflow.data.meta_dataset.MetaDataset` to MLflow, i.e., you want\n    to log the source of dataset to MLflow without loading the dataset.\n\n    Args:\n        path: the UC path of your data. It should be a valid UC path following the pattern\n            \"/Volumes/{catalog}/{schema}/{volume}/{file_path}\". For example,\n            \"/Volumes/MyCatalog/MySchema/MyVolume/MyFile.json\".\n    \"\"\"\n\n    def __init__(self, path: str):\n        self._verify_uc_path_is_valid(path)\n        self.path = path\n\n    def _verify_uc_path_is_valid(self, path):\n        \"\"\"Verify if the path exists in Databricks Unified Catalog.\"\"\"\n        try:\n            from databricks.sdk import WorkspaceClient\n\n            w = WorkspaceClient()\n        except ImportError:\n            _logger.warning(\n                \"Cannot verify the path of `UCVolumeDatasetSource` because of missing\"\n                \"`databricks-sdk`. Please install `databricks-sdk` via \"\n                \"`pip install -U databricks-sdk`. This does not block creating \"\n                \"`UCVolumeDatasetSource`, but your `UCVolumeDatasetSource` might be invalid.\"\n            )\n            return\n        except Exception:\n            _logger.warning(\n                \"Cannot verify the path of `UCVolumeDatasetSource` due to a connection failure \"\n                \"with Databricks workspace. Please run `mlflow.login()` to log in to Databricks. \"\n                \"This does not block creating `UCVolumeDatasetSource`, but your \"\n                \"`UCVolumeDatasetSource` might be invalid.\"\n            )\n            return\n\n        try:\n            w.files.get_metadata(path)\n        except Exception:\n            raise MlflowException(f\"{path} does not exist in Databricks Unified Catalog.\")\n\n    @staticmethod\n    def _get_source_type() -> str:\n        return \"uc_volume\"\n\n    @staticmethod\n    def _can_resolve(raw_source: Any):\n        raise NotImplementedError\n\n    @classmethod\n    def _resolve(cls, raw_source: str):\n        raise NotImplementedError\n\n    def to_dict(self) -> Dict[Any, Any]:\n        return {\"path\": self.path}\n\n    @classmethod\n    def from_dict(cls, source_dict: Dict[Any, Any]) -> \"UCVolumeDatasetSource\":\n        return cls(**source_dict)\n", "path": "mlflow/data/uc_volume_dataset_source.py"}], "after_files": [{"content": "import logging\nfrom typing import Any, Dict\n\nfrom mlflow.data.dataset_source import DatasetSource\nfrom mlflow.exceptions import MlflowException\n\n_logger = logging.getLogger(__name__)\n\n\nclass UCVolumeDatasetSource(DatasetSource):\n    \"\"\"Represents the source of a dataset stored in Databricks Unified Catalog Volume.\n\n    If you are using a delta table, please use `mlflow.data.delta_dataset_source.DeltaDatasetSource`\n    instead. This `UCVolumeDatasetSource` does not provide loading function, and is mostly useful\n    when you are logging a `mlflow.data.meta_dataset.MetaDataset` to MLflow, i.e., you want\n    to log the source of dataset to MLflow without loading the dataset.\n\n    Args:\n        path: the UC path of your data. It should be a valid UC path following the pattern\n            \"/Volumes/{catalog}/{schema}/{volume}/{file_path}\". For example,\n            \"/Volumes/MyCatalog/MySchema/MyVolume/MyFile.json\".\n    \"\"\"\n\n    def __init__(self, path: str):\n        self.path = path\n        self._verify_uc_path_is_valid()\n\n    def _verify_uc_path_is_valid(self):\n        \"\"\"Verify if the path exists in Databricks Unified Catalog.\"\"\"\n        try:\n            from databricks.sdk import WorkspaceClient\n\n            w = WorkspaceClient()\n        except ImportError:\n            _logger.warning(\n                \"Cannot verify the path of `UCVolumeDatasetSource` because of missing\"\n                \"`databricks-sdk`. Please install `databricks-sdk` via \"\n                \"`pip install -U databricks-sdk`. This does not block creating \"\n                \"`UCVolumeDatasetSource`, but your `UCVolumeDatasetSource` might be invalid.\"\n            )\n            return\n        except Exception:\n            _logger.warning(\n                \"Cannot verify the path of `UCVolumeDatasetSource` due to a connection failure \"\n                \"with Databricks workspace. Please run `mlflow.login()` to log in to Databricks. \"\n                \"This does not block creating `UCVolumeDatasetSource`, but your \"\n                \"`UCVolumeDatasetSource` might be invalid.\"\n            )\n            return\n\n        try:\n            # Check if `self.path` points to a valid UC file.\n            w.files.get_metadata(self.path)\n        except Exception:\n            try:\n                # Check if `self.path` points to a valid UC directory.\n                w.files.get_directory_metadata(self.path)\n                # Append a slash to `self.path` to indicate it's a directory.\n                self.path += \"/\" if not self.path.endswith(\"/\") else \"\"\n            except Exception:\n                # Neither file nor directory exists, we throw an exception.\n                raise MlflowException(f\"{self.path} does not exist in Databricks Unified Catalog.\")\n\n    @staticmethod\n    def _get_source_type() -> str:\n        return \"uc_volume\"\n\n    @staticmethod\n    def _can_resolve(raw_source: Any):\n        raise NotImplementedError\n\n    @classmethod\n    def _resolve(cls, raw_source: str):\n        raise NotImplementedError\n\n    def to_dict(self) -> Dict[Any, Any]:\n        return {\"path\": self.path}\n\n    @classmethod\n    def from_dict(cls, source_dict: Dict[Any, Any]) -> \"UCVolumeDatasetSource\":\n        return cls(**source_dict)\n", "path": "mlflow/data/uc_volume_dataset_source.py"}]}