Client Models

Optimeering

class optimeering.models.AccessKeyCreated(**data)
Parameters:

  • created_at (datetime) – Time stamp at which key was created.

  • description (str) – Description for the Access key.

  • expires_at (datetime) – Duration after which key expires.

  • id (int) – ID of the access key

classmethod from_dict(obj)

Create an instance of AccessKeyCreated from a dict

Return type:

Optional[AccessKeyCreated]

classmethod from_json(json_str)

Create an instance of AccessKeyCreated from a JSON string

Return type:

Optional[AccessKeyCreated]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas()

Converts the object into a pandas dataframe.

Return type:

DataFrame

to_polars()

Converts the object into a polars dataframe.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.AccessKeyListKeyResponse(**data)
Parameters:

  • items (List[AccessKeyCreated]) – List of access keys.

  • next_page (str) – The next page of results (if available).

filter(id=None)

Filters items

Return type:

AccessKeyListKeyResponse

classmethod from_dict(obj)

Create an instance of AccessKeyListKeyResponse from a dict

Return type:

Optional[AccessKeyListKeyResponse]

classmethod from_json(json_str)

Create an instance of AccessKeyListKeyResponse from a JSON string

Return type:

Optional[AccessKeyListKeyResponse]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas()

Converts the object into a pandas dataframe.

Return type:

DataFrame

to_polars()

Converts the object into a polars dataframe.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.AccessKeyPostResponse(**data)
Parameters:

  • apikey (str) – API key

  • created_at (datetime) – Time stamp at which key was created.

  • description (str) – Description for the Access key.

  • expires_at (datetime) – Duration after which key expires.

  • id (int) – ID of the access key

  • owner_id (str) – Creator of the access key.

classmethod from_dict(obj)

Create an instance of AccessKeyPostResponse from a dict

Return type:

Optional[AccessKeyPostResponse]

classmethod from_json(json_str)

Create an instance of AccessKeyPostResponse from a JSON string

Return type:

Optional[AccessKeyPostResponse]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas()

Converts the object into a pandas dataframe.

Return type:

DataFrame

to_polars()

Converts the object into a polars dataframe.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.AccessPostKey(**data)
Parameters:

  • description (str) – Description for the Access key.

  • expires_at (ExpiresAt)

classmethod from_dict(obj)

Create an instance of AccessPostKey from a dict

Return type:

Optional[AccessPostKey]

classmethod from_json(json_str)

Create an instance of AccessPostKey from a JSON string

Return type:

Optional[AccessPostKey]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas()

Converts the object into a pandas dataframe.

Return type:

DataFrame

to_polars()

Converts the object into a polars dataframe.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.EventTimeEnd(*args, **kwargs)

The last datetime to fetch (exclusive). This filter applies to event_time. Defaults to 2999-12-30 00:00:00+0000. Should be specified in ISO 8601 datetime or duration format (eg - 2024-05-15T06:00:00+00:00, PT1H, -P1W1D)

classmethod from_json(json_str)

Returns the object represented by the json string

Return type:

EventTimeEnd

to_dict()

Returns the dict representation of the actual instance

Return type:

Union[Dict[str, Any], datetime, str, None]

to_json()

Returns the JSON representation of the actual instance

Return type:

str

to_str()

Returns the string representation of the actual instance

Return type:

str

class optimeering.models.EventTimeStart(*args, **kwargs)

The first datetime to fetch (inclusive). This filter applies to event_time. Defaults to 1970-01-01 00:00:00+0000. Should be specified in ISO 8601 datetime or duration format (eg - 2024-05-15T06:00:00+00:00, PT1H, -P1W1D)

classmethod from_json(json_str)

Returns the object represented by the json string

Return type:

EventTimeStart

to_dict()

Returns the dict representation of the actual instance

Return type:

Union[Dict[str, Any], datetime, str, None]

to_json()

Returns the JSON representation of the actual instance

Return type:

str

to_str()

Returns the string representation of the actual instance

Return type:

str

class optimeering.models.ExpiresAt(*args, **kwargs)

Duration after which key expires. Defaults to one year in the future.

classmethod from_json(json_str)

Returns the object represented by the json string

Return type:

ExpiresAt

to_dict()

Returns the dict representation of the actual instance

Return type:

Union[Dict[str, Any], datetime, str, None]

to_json()

Returns the JSON representation of the actual instance

Return type:

str

to_str()

Returns the string representation of the actual instance

Return type:

str

class optimeering.models.HTTPValidationError(**data)
Parameters:

detail (List[ValidationError])

filter(msg=None, type=None)

Filters items

Return type:

HTTPValidationError

classmethod from_dict(obj)

Create an instance of HTTPValidationError from a dict

Return type:

Optional[HTTPValidationError]

classmethod from_json(json_str)

Create an instance of HTTPValidationError from a JSON string

Return type:

Optional[HTTPValidationError]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas()

Converts the object into a pandas dataframe.

Return type:

DataFrame

to_polars()

Converts the object into a polars dataframe.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.MaxEventTime(*args, **kwargs)

If specified, will only return the latest prediction available at the specified time. If not specified, no filtering on event_time is done. Should be specified in ISO 8601 datetime or duration format (eg - 2024-05-15T06:00:00+00:00, PT1H, -P1W1D)

classmethod from_json(json_str)

Returns the object represented by the json string

Return type:

MaxEventTime

to_dict()

Returns the dict representation of the actual instance

Return type:

Union[Dict[str, Any], datetime, str, None]

to_json()

Returns the JSON representation of the actual instance

Return type:

str

to_str()

Returns the string representation of the actual instance

Return type:

str

class optimeering.models.PredictionsData(**data)

PredictionsData contains a collection of PredictionsEvent for a given PredictionSeries

Parameters:

  • events (List[PredictionsEvent])

  • series_id (int) – Identifier for the series id.

  • version (str) – Version of the model that generated the predictions

filter(is_simulated=None)

Filters items

Return type:

PredictionsData

classmethod from_dict(obj)

Create an instance of PredictionsData from a dict

Return type:

Optional[PredictionsData]

classmethod from_json(json_str)

Create an instance of PredictionsData from a JSON string

Return type:

Optional[PredictionsData]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas(unpack_value_method)

Converts the object into a pandas dataframe.

Parameters:

unpack_value_method (str) –

Determines how values are unpacked. Should be one of the following:

  1. retain_original: Do not unpack the values.

  2. new_rows: A new row will be created in the dataframe for each unpacked value. A new column value_category will be added which determines the category of the value.

  3. new_columns: A new column will be created in the dataframe for each unpacked value. The columns for unpacked values will be prepended with value_.

Return type:

DataFrame

to_polars(unpack_value_method=None)

Converts the object into a polars dataframe.

Parameters:

unpack_value_method (str) –

Determines how values are unpacked. Should be one of the following:

  1. retain_original: Do not unpack the values.

  2. new_rows: A new row will be created in the dataframe for each unpacked value. A new column value_category will be added which determines the category of the value.

  3. new_columns: A new column will be created in the dataframe for each unpacked value. The columns for unpacked values will be prepended with value_.

to_str()

Returns the string representation of the model using alias

Return type:

str

classmethod version_validate_regular_expression(value)

Validates the regular expression

class optimeering.models.PredictionsDataList(**data)

A PredictionsDataList contains a collection of PredictionsData

Parameters:

  • items (List[PredictionsData])

  • next_page (str) – The next page of results (if available).

filter(series_id=None, version=None)

Filters items

Return type:

PredictionsDataList

classmethod from_dict(obj)

Create an instance of PredictionsDataList from a dict

Return type:

Optional[PredictionsDataList]

classmethod from_json(json_str)

Create an instance of PredictionsDataList from a JSON string

Return type:

Optional[PredictionsDataList]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas(unpack_value_method)

Converts the object into a pandas dataframe.

Parameters:

unpack_value_method (str) –

Determines how values are unpacked. Should be one of the following:

  1. retain_original: Do not unpack the values.

  2. new_rows: A new row will be created in the dataframe for each unpacked value. A new column value_category will be added which determines the category of the value.

  3. new_columns: A new column will be created in the dataframe for each unpacked value. The columns for unpacked values will be prepended with value_.

Return type:

DataFrame

to_polars(unpack_value_method=None)

Converts the object into a polars dataframe.

Parameters:

unpack_value_method (str) –

Determines how values are unpacked. Should be one of the following:

  1. retain_original: Do not unpack the values.

  2. new_rows: A new row will be created in the dataframe for each unpacked value. A new column value_category will be added which determines the category of the value.

  3. new_columns: A new column will be created in the dataframe for each unpacked value. The columns for unpacked values will be prepended with value_.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.PredictionsEvent(**data)

A PredictionsEvent contains a collection of PredictionsValue. If a PredictionsEvent is simulated, is_simulated will be true. See Prediction Versioning for an explanation on what simulated events are.

Parameters:

  • created_at (datetime) – The timestamp at which datapoint was registered

  • event_time (datetime) – Timestamp for when datapoint was generated.

  • is_simulated (bool)

  • predictions (List[PredictionsValue])

classmethod from_dict(obj)

Create an instance of PredictionsEvent from a dict

Return type:

Optional[PredictionsEvent]

classmethod from_json(json_str)

Create an instance of PredictionsEvent from a JSON string

Return type:

Optional[PredictionsEvent]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas(unpack_value_method)

Converts the object into a pandas dataframe.

Parameters:

unpack_value_method (str) –

Determines how values are unpacked. Should be one of the following:

  1. retain_original: Do not unpack the values.

  2. new_rows: A new row will be created in the dataframe for each unpacked value. A new column value_category will be added which determines the category of the value.

  3. new_columns: A new column will be created in the dataframe for each unpacked value. The columns for unpacked values will be prepended with value_.

Return type:

DataFrame

to_polars(unpack_value_method=None)

Converts the object into a polars dataframe.

Parameters:

unpack_value_method (str) –

Determines how values are unpacked. Should be one of the following:

  1. retain_original: Do not unpack the values.

  2. new_rows: A new row will be created in the dataframe for each unpacked value. A new column value_category will be added which determines the category of the value.

  3. new_columns: A new column will be created in the dataframe for each unpacked value. The columns for unpacked values will be prepended with value_.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.PredictionsSeries(**data)

A PredictionsSeries is used for indexing a series of PredictionsData.

Parameters:

  • area (str) – Areas to be filtered. E.g. NO1, NO2

  • created_at (datetime)

  • description (str)

  • id (int)

  • latest_event_time (datetime)

  • product (str) – Product name for the series

  • resolution (str) – Resolution of the series.

  • statistic (str) – Type of statistics.

  • unit (str) – The unit for the series.

  • unit_type (str) – Unit type for the series

  • version (str)

datapoints(start=None, end=None)

Returns predictions.

If multiple versions of a prediction exist for a given series, the highest version is returned.

To get predictions for a particular version, use the retrieve_versioned method.

Return type:

PredictionsDataList

classmethod from_dict(obj)

Create an instance of PredictionsSeries from a dict

Return type:

Optional[PredictionsSeries]

classmethod from_json(json_str)

Create an instance of PredictionsSeries from a JSON string

Return type:

Optional[PredictionsSeries]

list_version(product=None, unit_type=None, statistic=None, area=None, resolution=None)

Returns prediction series and their versions.

Return type:

PredictionsVersionList

retrieve(start=None, end=None)

Returns predictions.

If multiple versions of a prediction exist for a given series, the highest version is returned.

To get predictions for a particular version, use the retrieve_versioned method.

Return type:

PredictionsDataList

retrieve_latest(max_event_time=None)

Returns predictions with the most recent event_time.

If multiple versions of a prediction exist for a given series, the highest version is returned.

To get predictions for a particular version, use the retrieve_versioned method.

Return type:

PredictionsDataList

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas()

Converts the object into a pandas dataframe.

Return type:

DataFrame

to_polars()

Converts the object into a polars dataframe.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.PredictionsSeriesList(**data)

A PredictionsSeriesList contains a collection of PredictionsSeries.

Parameters:

  • items (List[PredictionsSeries])

  • next_page (str) – The next page of results (if available).

datapoints(start=None, end=None)

Returns predictions.

If multiple versions of a prediction exist for a given series, the highest version is returned.

To get predictions for a particular version, use the retrieve_versioned method.

Return type:

PredictionsDataList

filter(area=None, id=None, product=None, resolution=None, statistic=None, unit=None, unit_type=None)

Filters items

Return type:

PredictionsSeriesList

classmethod from_dict(obj)

Create an instance of PredictionsSeriesList from a dict

Return type:

Optional[PredictionsSeriesList]

classmethod from_json(json_str)

Create an instance of PredictionsSeriesList from a JSON string

Return type:

Optional[PredictionsSeriesList]

list_version(product=None, unit_type=None, statistic=None, area=None, resolution=None)

Returns prediction series and their versions.

Return type:

PredictionsVersionList

retrieve(start=None, end=None)

Returns predictions.

If multiple versions of a prediction exist for a given series, the highest version is returned.

To get predictions for a particular version, use the retrieve_versioned method.

Return type:

PredictionsDataList

retrieve_latest(max_event_time=None)

Returns predictions with the most recent event_time.

If multiple versions of a prediction exist for a given series, the highest version is returned.

To get predictions for a particular version, use the retrieve_versioned method.

Return type:

PredictionsDataList

property series_ids

Returns all the series ids included in the response

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas()

Converts the object into a pandas dataframe.

Return type:

DataFrame

to_polars()

Converts the object into a polars dataframe.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.PredictionsValue(**data)

A PredictionsValue contains the value for a specific period of time as specified by the prediction_for datetime.

Parameters:

  • prediction_for (datetime) – The time prediction is made for.’

  • value (Dict[str, float])

classmethod from_dict(obj)

Create an instance of PredictionsValue from a dict

Return type:

Optional[PredictionsValue]

classmethod from_json(json_str)

Create an instance of PredictionsValue from a JSON string

Return type:

Optional[PredictionsValue]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas(unpack_value_method)

Converts the object into a pandas dataframe.

Parameters:

unpack_value_method (str) –

Determines how values are unpacked. Should be one of the following:

  1. retain_original: Do not unpack the values.

  2. new_rows: A new row will be created in the dataframe for each unpacked value. A new column value_category will be added which determines the category of the value.

  3. new_columns: A new column will be created in the dataframe for each unpacked value. The columns for unpacked values will be prepended with value_.

Return type:

DataFrame

to_polars(unpack_value_method=None)

Converts the object into a polars dataframe.

Parameters:

unpack_value_method (str) –

Determines how values are unpacked. Should be one of the following:

  1. retain_original: Do not unpack the values.

  2. new_rows: A new row will be created in the dataframe for each unpacked value. A new column value_category will be added which determines the category of the value.

  3. new_columns: A new column will be created in the dataframe for each unpacked value. The columns for unpacked values will be prepended with value_.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.PredictionsVersion(**data)

A PredictionsVersion is used for indexing a specific version for a series of PredictionsData. For an explanation on versioned and simulated data see Prediction Versioning

Parameters:

  • area (str) – Areas to be filtered. E.g. NO1, NO2

  • created_at (datetime)

  • description (str)

  • id (int)

  • latest_event_time (datetime)

  • latest_simulated_event_time (datetime)

  • product (str) – Product name for the series

  • resolution (str) – Resolution of the series.

  • simulation_event_time_end (datetime) – The timestamp to which predictions is generated using simulation

  • simulation_event_time_start (datetime) – The timestamp from which predictions is generated using simulation

  • statistic (str) – Type of statistics.

  • unit (str) – The unit for the series.

  • unit_type (str) – Unit type for the series

  • version (str) – Version of the model that generated the predictions

convert_to_versioned_series()

Convert to VersionedSeries

Return type:

VersionedSeries

classmethod from_dict(obj)

Create an instance of PredictionsVersion from a dict

Return type:

Optional[PredictionsVersion]

classmethod from_json(json_str)

Create an instance of PredictionsVersion from a JSON string

Return type:

Optional[PredictionsVersion]

retrieve_versioned(include_simulated=None, start=None, end=None)

Returns versioned predictions.

Use the list_version method to get the available versions for each prediction series.

Can be used to retrieve both versioned and simulated data. For an explanation on versioned and simulated data see Prediction Versioning

Return type:

PredictionsDataList

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas()

Converts the object into a pandas dataframe.

Return type:

DataFrame

to_polars()

Converts the object into a polars dataframe.

to_str()

Returns the string representation of the model using alias

Return type:

str

classmethod version_validate_regular_expression(value)

Validates the regular expression

class optimeering.models.PredictionsVersionList(**data)

A PredictionsVersionList contains a collection of PredictionsVersion.

Parameters:

  • items (List[PredictionsVersion])

  • next_page (str) – The next page of results (if available).

convert_to_versioned_series()

Converts all items

Return type:

List

filter(area=None, id=None, product=None, resolution=None, statistic=None, unit=None, unit_type=None, version=None)

Filters items

Return type:

PredictionsVersionList

classmethod from_dict(obj)

Create an instance of PredictionsVersionList from a dict

Return type:

Optional[PredictionsVersionList]

classmethod from_json(json_str)

Create an instance of PredictionsVersionList from a JSON string

Return type:

Optional[PredictionsVersionList]

retrieve_versioned(include_simulated=None, start=None, end=None)

Returns versioned predictions.

Use the list_version method to get the available versions for each prediction series.

Can be used to retrieve both versioned and simulated data. For an explanation on versioned and simulated data see Prediction Versioning

Return type:

PredictionsDataList

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas()

Converts the object into a pandas dataframe.

Return type:

DataFrame

to_polars()

Converts the object into a polars dataframe.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.ValidationError(**data)
Parameters:
classmethod from_dict(obj)

Create an instance of ValidationError from a dict

Return type:

Optional[ValidationError]

classmethod from_json(json_str)

Create an instance of ValidationError from a JSON string

Return type:

Optional[ValidationError]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas()

Converts the object into a pandas dataframe.

Return type:

DataFrame

to_polars()

Converts the object into a polars dataframe.

to_str()

Returns the string representation of the model using alias

Return type:

str

class optimeering.models.ValidationErrorLocInner(*args, **kwargs)
classmethod from_json(json_str)

Returns the object represented by the json string

Return type:

ValidationErrorLocInner

to_dict()

Returns the dict representation of the actual instance

Return type:

Union[Dict[str, Any], int, str, None]

to_json()

Returns the JSON representation of the actual instance

Return type:

str

to_str()

Returns the string representation of the actual instance

Return type:

str

class optimeering.models.VersionedSeries(**data)

A VersionedSeries can be used with retrieve_versioned to specify a version of a series to retrieve.

Parameters:

  • series_id (int) – Id of the series

  • version (str) – Version number of the series to filter

classmethod from_dict(obj)

Create an instance of VersionedSeries from a dict

Return type:

Optional[VersionedSeries]

classmethod from_json(json_str)

Create an instance of VersionedSeries from a JSON string

Return type:

Optional[VersionedSeries]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

to_json()

Returns the JSON representation of the model using alias

Return type:

str

to_pandas()

Converts the object into a pandas dataframe.

Return type:

DataFrame

to_polars()

Converts the object into a polars dataframe.

to_str()

Returns the string representation of the model using alias

Return type:

str

classmethod version_validate_regular_expression(value)

Validates the regular expression