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CoreyEWoodCoreyEWood
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update code based on latest backend changes
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‎generated/docs/BinaryClassificationResult.md‎

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@@ -7,6 +7,7 @@ Name | Type | Description | Notes
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**label** | **str** | |
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**confidence** | **float, none_type** | | [optional]
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**source** | **str** | | [optional]
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**result_type** | **str** | | [optional] if omitted the server will use the default value of "binary_classification"
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**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
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[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

‎generated/docs/CountingResult.md‎

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@@ -7,6 +7,7 @@ Name | Type | Description | Notes
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**count** | **int, none_type** | |
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**confidence** | **float, none_type** | | [optional]
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**source** | **str** | | [optional]
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**result_type** | **str** | | [optional] if omitted the server will use the default value of "counting"
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**greater_than_max** | **bool** | | [optional]
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**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
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‎generated/docs/LabelValueRequest.md‎

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## Properties
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Name | Type | Description | Notes
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------------ | ------------- | ------------- | -------------
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**label** | **str** | |
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**label** | **str, none_type** | |
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**image_query_id** | **str** | |
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**rois** | [**[ROIRequest], none_type**](ROIRequest.md) | | [optional]
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**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]

‎generated/docs/MultiClassificationResult.md‎

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@@ -7,6 +7,7 @@ Name | Type | Description | Notes
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**label** | **str** | |
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**confidence** | **float, none_type** | | [optional]
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**source** | **str** | | [optional]
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**result_type** | **str** | | [optional] if omitted the server will use the default value of "multi_classification"
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**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
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[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

‎generated/docs/SourceEnum.md‎

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@@ -1,11 +1,11 @@
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# SourceEnum
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3-
* `INITIAL_PLACEHOLDER` - InitialPlaceholder * `CLOUD` - HumanCloud * `CUST` - HumanCustomer * `HUMAN_CLOUD_ENSEMBLE` - HumanCloudEnsemble * `ALG` - Algorithm * `ALG_REC` - AlgorithmReconciled * `ALG_UNCLEAR` - AlgorithmUnclear
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* `INITIAL_PLACEHOLDER` - InitialPlaceholder * `CLOUD` - HumanCloud * `CUST` - HumanCustomer * `HUMAN_CLOUD_ENSEMBLE` - HumanCloudEnsemble * `ALG` - Algorithm * `ALG_REC` - AlgorithmReconciled * `ALG_UNCLEAR` - AlgorithmUnclear * `EDGE` - EDGE
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## Properties
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Name | Type | Description | Notes
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------------ | ------------- | ------------- | -------------
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**value** | **str** | * `INITIAL_PLACEHOLDER` - InitialPlaceholder * `CLOUD` - HumanCloud * `CUST` - HumanCustomer * `HUMAN_CLOUD_ENSEMBLE` - HumanCloudEnsemble * `ALG` - Algorithm * `ALG_REC` - AlgorithmReconciled * `ALG_UNCLEAR` - AlgorithmUnclear | must be one of ["INITIAL_PLACEHOLDER", "CLOUD", "CUST", "HUMAN_CLOUD_ENSEMBLE", "ALG", "ALG_REC", "ALG_UNCLEAR", ]
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**value** | **str** | * `INITIAL_PLACEHOLDER` - InitialPlaceholder * `CLOUD` - HumanCloud * `CUST` - HumanCustomer * `HUMAN_CLOUD_ENSEMBLE` - HumanCloudEnsemble * `ALG` - Algorithm * `ALG_REC` - AlgorithmReconciled * `ALG_UNCLEAR` - AlgorithmUnclear * `EDGE` - EDGE | must be one of ["INITIAL_PLACEHOLDER", "CLOUD", "CUST", "HUMAN_CLOUD_ENSEMBLE", "ALG", "ALG_REC", "ALG_UNCLEAR", "EDGE", ]
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[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)
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‎generated/docs/TextRecognitionResult.md‎

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## Properties
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Name | Type | Description | Notes
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------------ | ------------- | ------------- | -------------
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**text** | **str** | |
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**text** | **str, none_type** | |
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**truncated** | **bool** | |
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**confidence** | **float, none_type** | | [optional]
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**source** | **str** | | [optional]
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**result_type** | **str** | | [optional] if omitted the server will use the default value of "text_recognition"
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**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
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[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

‎generated/groundlight_openapi_client/model/binary_classification_result.py‎

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@@ -65,6 +65,10 @@ class BinaryClassificationResult(ModelNormal):
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"USER": "USER",
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"CLOUD_ENSEMBLE": "CLOUD_ENSEMBLE",
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"ALGORITHM": "ALGORITHM",
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"EDGE": "EDGE",
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},
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("result_type",): {
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"BINARY_CLASSIFICATION": "binary_classification",
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},
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}
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none_type,
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), # noqa: E501
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"source": (str,), # noqa: E501
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"result_type": (str,), # noqa: E501
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}
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@cached_property
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"label": "label", # noqa: E501
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"confidence": "confidence", # noqa: E501
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"source": "source", # noqa: E501
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"result_type": "result_type", # noqa: E501
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}
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read_only_vars = {}
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_visited_composed_classes = (Animal,)
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confidence (float, none_type): [optional] # noqa: E501
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source (str): [optional] # noqa: E501
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result_type (str): [optional] if omitted the server will use the default value of "binary_classification" # noqa: E501
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"""
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_check_type = kwargs.pop("_check_type", True)
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_visited_composed_classes = (Animal,)
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confidence (float, none_type): [optional] # noqa: E501
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source (str): [optional] # noqa: E501
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result_type (str): [optional] if omitted the server will use the default value of "binary_classification" # noqa: E501
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"""
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_check_type = kwargs.pop("_check_type", True)

‎generated/groundlight_openapi_client/model/counting_result.py‎

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@@ -60,6 +60,10 @@ class CountingResult(ModelNormal):
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"USER": "USER",
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"CLOUD_ENSEMBLE": "CLOUD_ENSEMBLE",
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"ALGORITHM": "ALGORITHM",
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"EDGE": "EDGE",
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},
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("result_type",): {
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"COUNTING": "counting",
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},
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}
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none_type,
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), # noqa: E501
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"source": (str,), # noqa: E501
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"result_type": (str,), # noqa: E501
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"greater_than_max": (bool,), # noqa: E501
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}
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"count": "count", # noqa: E501
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"confidence": "confidence", # noqa: E501
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"source": "source", # noqa: E501
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"result_type": "result_type", # noqa: E501
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"greater_than_max": "greater_than_max", # noqa: E501
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}
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_visited_composed_classes = (Animal,)
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confidence (float, none_type): [optional] # noqa: E501
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source (str): [optional] # noqa: E501
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result_type (str): [optional] if omitted the server will use the default value of "counting" # noqa: E501
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greater_than_max (bool): [optional] # noqa: E501
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"""
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_visited_composed_classes = (Animal,)
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confidence (float, none_type): [optional] # noqa: E501
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source (str): [optional] # noqa: E501
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result_type (str): [optional] if omitted the server will use the default value of "counting" # noqa: E501
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greater_than_max (bool): [optional] # noqa: E501
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"""
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‎generated/groundlight_openapi_client/model/label_value_request.py‎

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"""
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lazy_import()
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return {
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"label": (str,), # noqa: E501
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"label": (
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str,
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none_type,
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), # noqa: E501
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"image_query_id": (str,), # noqa: E501
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"rois": (
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[ROIRequest],
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"""LabelValueRequest - a model defined in OpenAPI
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label (str):
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label (str, none_type):
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image_query_id (str):
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Keyword Args:
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"""LabelValueRequest - a model defined in OpenAPI
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label (str):
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label (str, none_type):
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image_query_id (str):
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Keyword Args:

‎generated/groundlight_openapi_client/model/multi_classification_result.py‎

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"USER": "USER",
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"CLOUD_ENSEMBLE": "CLOUD_ENSEMBLE",
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"ALGORITHM": "ALGORITHM",
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"EDGE": "EDGE",
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},
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("result_type",): {
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"MULTI_CLASSIFICATION": "multi_classification",
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},
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}
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none_type,
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), # noqa: E501
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"source": (str,), # noqa: E501
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"result_type": (str,), # noqa: E501
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}
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@cached_property
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"label": "label", # noqa: E501
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"confidence": "confidence", # noqa: E501
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"source": "source", # noqa: E501
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"result_type": "result_type", # noqa: E501
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}
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read_only_vars = {}
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_visited_composed_classes = (Animal,)
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confidence (float, none_type): [optional] # noqa: E501
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source (str): [optional] # noqa: E501
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result_type (str): [optional] if omitted the server will use the default value of "multi_classification" # noqa: E501
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"""
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_check_type = kwargs.pop("_check_type", True)
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_visited_composed_classes = (Animal,)
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confidence (float, none_type): [optional] # noqa: E501
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source (str): [optional] # noqa: E501
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result_type (str): [optional] if omitted the server will use the default value of "multi_classification" # noqa: E501
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"""
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_check_type = kwargs.pop("_check_type", True)

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