Copy Run/Copy Model version doesn't work when trying to use with Databricks workspaces. Error 403 get raised.

[codekcg23]

I am running the example notebooks in Databricks to copy Mlflow run to another Databricks environment.
Databricks cluster ML Run 13.3 LTS
mlflow.version: 2.5.0

from mlflow_export_import.copy.copy_run import copy

dst_run = copy(src_run_id, dst_experiment_name, "databricks", dst_run_workspace)

Inputs
dst_experiment_name = /mlflow_experiments/test
dst_run_workspace = databricks://us_test:mlflow_central

Logs

20-Feb-24 18:08:52 - INFO - Exporting run: {'run_id': '7b9511eea1e64cfab0c8d8b61dac6cea', 'lifecycle_stage': 'active', 'experiment_id': '3355151502358774'}
20-Feb-24 18:08:53 - WARNING - No notebooks to export for run '7b9511eea1e64cfab0c8d8b61dac6cea' since tag 'mlflow.databricks.notebookPath' is not set.
20-Feb-24 18:08:53 - INFO - Importing run from '/tmp/tmpwvk_29vl'
20-Feb-24 18:08:53 - INFO - Creating Databricks workspace directory '/mlflow_experiments'

Error

ValueError: Enum ErrorCode has no value defined for name 403

ValueError Traceback (most recent call last)
File , line 3
1 from mlflow_export_import.copy.copy_run import copy
----> 3 dst_run = copy(src_run_id, dst_experiment_name, "databricks", dst_run_workspace)

File /Workspace/Repos/********/mlflow-export-import/mlflow_export_import/copy/copy_run.py:32, in copy(src_run_id, dst_experiment_name, src_mlflow_uri, dst_mlflow_uri)
15 def copy(
16 src_run_id,
17 dst_experiment_name,
18 src_mlflow_uri = None,
19 dst_mlflow_uri = None
20 ):
21 """
22 Copies a run to another tracking server (workspace).
23
(...)
29 :return: Destination Run object.
30 """
---> 32 return _copy(src_run_id, dst_experiment_name,
33 copy_utils.mk_client(src_mlflow_uri),
34 copy_utils.mk_client(dst_mlflow_uri)
35 )

File /Workspace/Repos/**********/mlflow-export-import/mlflow_export_import/copy/copy_run.py:48, in _copy(src_run_id, dst_experiment_name, src_client, dst_client)
41 with tempfile.TemporaryDirectory() as download_dir:
42 export_run(
43 src_run_id,
44 download_dir,
45 notebook_formats = [ "SOURCE" ],
46 mlflow_client = src_client
47 )
---> 48 dst_run, _ = import_run(
49 download_dir,
50 dst_experiment_name,
51 mlflow_client = dst_client
52 )
53 return dst_run

File /Workspace/Repos/********/mlflow-export-import/mlflow_export_import/run/import_run.py:67, in import_run(input_dir, experiment_name, import_source_tags, dst_notebook_dir, use_src_user_id, mlmodel_fix, mlflow_client)
63 dbx_client = create_dbx_client(mlflow_client)
65 _logger.info(f"Importing run from '{input_dir}'")
---> 67 exp = mlflow_utils.set_experiment(mlflow_client, dbx_client, experiment_name)
68 src_run_path = os.path.join(input_dir, "run.json")
69 src_run_dct = io_utils.read_file_mlflow(src_run_path)

File /Workspace/Repos/*********/mlflow-export-import/mlflow_export_import/common/mlflow_utils.py:35, in set_experiment(mlflow_client, dbx_client, exp_name, tags)
33 if not tags: tags = {}
34 tags = utils.create_mlflow_tags_for_databricks_import(tags)
---> 35 exp_id = mlflow_client.create_experiment(exp_name, tags=tags)
36 exp = mlflow_client.get_experiment(exp_id)
37 _logger.info(f"Created experiment '{exp.name}' with location '{exp.artifact_location}'")

File /databricks/python/lib/python3.10/site-packages/mlflow/tracking/client.py:557, in MlflowClient.create_experiment(self, name, artifact_location, tags)
509 def create_experiment(
510 self,
511 name: str,
512 artifact_location: Optional[str] = None,
513 tags: Optional[Dict[str, Any]] = None,
514 ) -> str:
515 """Create an experiment.
516
517 :param name: The experiment name. Must be unique.
(...)
555 Lifecycle_stage: active
556 """
--> 557 return self._tracking_client.create_experiment(name, artifact_location, tags)

File /databricks/python/lib/python3.10/site-packages/mlflow/tracking/_tracking_service/client.py:236, in TrackingServiceClient.create_experiment(self, name, artifact_location, tags)
225 """Create an experiment.
226
227 :param name: The experiment name. Must be unique.
(...)
232 :return: Integer ID of the created experiment.
233 """
234 _validate_experiment_artifact_location(artifact_location)
--> 236 return self.store.create_experiment(
237 name=name,
238 artifact_location=artifact_location,
239 tags=[ExperimentTag(key, value) for (key, value) in tags.items()] if tags else [],
240 )

File /databricks/python/lib/python3.10/site-packages/mlflow/store/tracking/rest_store.py:98, in RestStore.create_experiment(self, name, artifact_location, tags)
94 tag_protos = [tag.to_proto() for tag in tags] if tags else []
95 req_body = message_to_json(
96 CreateExperiment(name=name, artifact_location=artifact_location, tags=tag_protos)
97 )
---> 98 response_proto = self._call_endpoint(CreateExperiment, req_body)
99 return response_proto.experiment_id

File /databricks/python/lib/python3.10/site-packages/mlflow/store/tracking/rest_store.py:59, in RestStore._call_endpoint(self, api, json_body)
57 endpoint, method = _METHOD_TO_INFO[api]
58 response_proto = api.Response()
---> 59 return call_endpoint(self.get_host_creds(), endpoint, method, json_body, response_proto)

File /databricks/python/lib/python3.10/site-packages/mlflow/utils/rest_utils.py:203, in call_endpoint(host_creds, endpoint, method, json_body, response_proto, extra_headers)
201 call_kwargs["json"] = json_body
202 response = http_request(**call_kwargs)
--> 203 response = verify_rest_response(response, endpoint)
204 js_dict = json.loads(response.text)
205 parse_dict(js_dict=js_dict, message=response_proto)

File /databricks/python/lib/python3.10/site-packages/mlflow/utils/rest_utils.py:135, in verify_rest_response(response, endpoint)
133 if response.status_code != 200:
134 if _can_parse_as_json_object(response.text):
--> 135 raise RestException(json.loads(response.text))
136 else:
137 base_msg = (
138 f"API request to endpoint {endpoint} "
139 f"failed with error code {response.status_code} != 200"
140 )

File /databricks/python/lib/python3.10/site-packages/mlflow/exceptions.py:122, in RestException.init(self, json)
117 error_code = json.get("error_code", ErrorCode.Name(INTERNAL_ERROR))
118 message = "{}: {}".format(
119 error_code,
120 json["message"] if "message" in json else "Response: " + str(json),
121 )
--> 122 super().init(message, error_code=ErrorCode.Value(error_code))
123 self.json = json

File /databricks/python/lib/python3.10/site-packages/google/protobuf/internal/enum_type_wrapper.py:73, in EnumTypeWrapper.Value(self, name)
71 except KeyError:
72 pass # fall out to break exception chaining
---> 73 raise ValueError('Enum {} has no value defined for name {!r}'.format(
74 self._enum_type.name, name))

ValueError: Enum ErrorCode has no value defined for name 403

Can anyone help with this error?