​
Classes

​
ClassifyApi

edgeimpulse_api.api.classify_api.ClassifyApi(
	api_client=None
)
Parameters
api_client=None
METHODS

​
classify_image

edgeimpulse_api.api.classify_api.ClassifyApi.classify_image(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	image: Annotated[str, Strict(strict=True)],
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.test_pretrained_model_response.TestPretrainedModelResponse
Classify an image Test out a trained impulse (using a posted image).
Parameters
self
project_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]
imageAnnotated[str, Strict(strict=True)]
impulse_idAnnotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None
**kwargs
Returns
edgeimpulse_api.models.test_pretrained_model_response.TestPretrainedModelResponse

​
classify_sample

edgeimpulse_api.api.classify_api.ClassifyApi.classify_sample(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	sample_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')],
	include_debug_info: Annotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Whether to return the debug information from FOMO classification.')] = None,
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.classify_sample_response.ClassifySampleResponse
Classify sample (deprecated) This API is deprecated, use classifySampleV2 instead (/v1/api/{projectId}/classify/v2/{sampleId}). Classify a complete file against the current impulse. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted.
Parameters
self
project_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]
sample_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')]
include_debug_infoAnnotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Whether to return the debug information from FOMO classification.')] = None
impulse_idAnnotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None
**kwargs
Returns
edgeimpulse_api.models.classify_sample_response.ClassifySampleResponse

​
classify_sample_by_learn_block

edgeimpulse_api.api.classify_api.ClassifyApi.classify_sample_by_learn_block(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	sample_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')],
	block_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Block ID')],
	**kwargs
) ‑> edgeimpulse_api.models.classify_sample_response.ClassifySampleResponse
Classify sample by learn block This API is deprecated, use classifySampleByLearnBlockV2 (/v1/api/{projectId}/classify/anomaly-gmm/v2/{blockId}/{sampleId}) instead. Classify a complete file against the specified learn block. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted.
Parameters
self
project_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]
sample_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')]
block_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Block ID')]
**kwargs
Returns
edgeimpulse_api.models.classify_sample_response.ClassifySampleResponse

​
classify_sample_by_learn_block_v2

edgeimpulse_api.api.classify_api.ClassifyApi.classify_sample_by_learn_block_v2(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	sample_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')],
	block_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Block ID')],
	variant: Annotated[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum | None, FieldInfo(annotation=NoneType, required=True, description='Keras model variant')] = None,
	truncate_structured_labels: Annotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.classify_sample_v2200_response.ClassifySampleV2200Response
Classify sample by learn block Classify a complete file against the specified learn block. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted. Depending on the size of your file, whether your sample is resampled, and whether the result is cached you’ll get either the result or a job back. If you receive a job, then wait for the completion of the job, and then call this function again to receive the results. The unoptimized (float32) model is used by default, and classification with an optimized (int8) model can be slower.
Parameters
self
project_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]
sample_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')]
block_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Block ID')]
variantAnnotated[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum | None, FieldInfo(annotation=NoneType, required=True, description='Keras model variant')] = None
truncate_structured_labelsAnnotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None
**kwargs
Returns
edgeimpulse_api.models.classify_sample_v2200_response.ClassifySampleV2200Response

​
classify_sample_for_variants

edgeimpulse_api.api.classify_api.ClassifyApi.classify_sample_for_variants(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	sample_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')],
	variants: Annotated[str, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='List of keras model variants, given as a JSON string')],
	include_debug_info: Annotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Whether to return the debug information from FOMO classification.')] = None,
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	truncate_structured_labels: Annotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.classify_sample_for_variants200_response.ClassifySampleForVariants200Response
Classify sample for the given set of variants Classify a complete file against the current impulse, for all given variants. Depending on the size of your file and whether the sample is resampled, you may get a job ID in the response.
Parameters
self
project_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]
sample_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')]
variantsAnnotated[str, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='List of keras model variants, given as a JSON string')]
include_debug_infoAnnotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Whether to return the debug information from FOMO classification.')] = None
impulse_idAnnotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None
truncate_structured_labelsAnnotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None
**kwargs
Returns
edgeimpulse_api.models.classify_sample_for_variants200_response.ClassifySampleForVariants200Response

​
classify_sample_v2

edgeimpulse_api.api.classify_api.ClassifyApi.classify_sample_v2(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	sample_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')],
	include_debug_info: Annotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Whether to return the debug information from FOMO classification.')] = None,
	variant: Annotated[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum | None, FieldInfo(annotation=NoneType, required=True, description='Keras model variant')] = None,
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	truncate_structured_labels: Annotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.classify_sample_v2200_response.ClassifySampleV2200Response
Classify sample Classify a complete file against the current impulse. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted. Depending on the size of your file, whether your sample is resampled, and whether the result is cached you’ll get either the result or a job back. If you receive a job, then wait for the completion of the job, and then call this function again to receive the results. The unoptimized (float32) model is used by default, and classification with an optimized (int8) model can be slower.
Parameters
self
project_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]
sample_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')]
include_debug_infoAnnotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Whether to return the debug information from FOMO classification.')] = None
variantAnnotated[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum | None, FieldInfo(annotation=NoneType, required=True, description='Keras model variant')] = None
impulse_idAnnotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None
truncate_structured_labelsAnnotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None
**kwargs
Returns
edgeimpulse_api.models.classify_sample_v2200_response.ClassifySampleV2200Response

​
get_classify_job_result

edgeimpulse_api.api.classify_api.ClassifyApi.get_classify_job_result(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	feature_explorer_only: Annotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Whether to get only the classification results relevant to the feature explorer.')] = None,
	variant: Annotated[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum | None, FieldInfo(annotation=NoneType, required=True, description='Keras model variant')] = None,
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	truncate_structured_labels: Annotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.classify_job_response.ClassifyJobResponse
Classify job result Get classify job result, containing the result for the complete testing dataset.
Parameters
self
project_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]
feature_explorer_onlyAnnotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Whether to get only the classification results relevant to the feature explorer.')] = None
variantAnnotated[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum | None, FieldInfo(annotation=NoneType, required=True, description='Keras model variant')] = None
impulse_idAnnotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None
truncate_structured_labelsAnnotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None
**kwargs
Returns
edgeimpulse_api.models.classify_job_response.ClassifyJobResponse

​
get_classify_job_result_page

edgeimpulse_api.api.classify_api.ClassifyApi.get_classify_job_result_page(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	limit: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Maximum number of results')] = None,
	offset: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Offset in results, can be used in conjunction with LimitResultsParameter to implement paging.')] = None,
	variant: Annotated[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum | None, FieldInfo(annotation=NoneType, required=True, description='Keras model variant')] = None,
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	truncate_structured_labels: Annotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None,
	labels: Annotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples with a label within the given list of labels, given as a JSON string')] = None,
	filename: Annotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples whose filename includes the given filename')] = None,
	max_length: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples shorter than the given length, in milliseconds')] = None,
	min_length: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples longer than the given length, in milliseconds')] = None,
	min_frequency: Annotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples with higher frequency than given frequency, in hertz')] = None,
	max_frequency: Annotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples with lower frequency than given frequency, in hertz')] = None,
	signature_validity: Annotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Include samples with either valid or invalid signatures')] = None,
	min_label: Annotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples with a label >= this value')] = None,
	max_label: Annotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples with a label < this value')] = None,
	search: Annotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Search query')] = None,
	data_type: Annotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Include only samples with a particular data type')] = None,
	min_id: Annotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Include only samples with an ID >= this value')] = None,
	max_id: Annotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Include only samples with an ID < this value')] = None,
	metadata: Annotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Filter samples by metadata key-value pairs, provided as a JSON string. Each item in the filter list is an object with the following properties:     - "key": Metadata key to filter on.     - "op": Operator ("eq" for positive match, "neq" for negative match).     - "values": (optional) Array of values to match/exclude. If omitted or empty, matches/excludes all values for the key. In addition to filter objects, the following option objects can be specified:     - { "no_metadata": boolean } - If true, include samples without any metadata     - { "filters_combinator": ("and" | "or") } - Specifies the combinator and matching mode:         - "and": All filter items must match (logical AND).         - "or": Any filter item may match (logical OR); samples with metadata keys not present in the filters are included. ')] = None,
	min_date: Annotated[datetime.datetime | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples that where added after the date given')] = None,
	max_date: Annotated[datetime.datetime | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples that were added before the date given')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.classify_job_response_page.ClassifyJobResponsePage
Single page of a classify job result Get classify job result, containing the predictions for a given page.
Parameters
self
project_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]
limitAnnotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Maximum number of results')] = None
offsetAnnotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Offset in results, can be used in conjunction with LimitResultsParameter to implement paging.')] = None
variantAnnotated[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum | None, FieldInfo(annotation=NoneType, required=True, description='Keras model variant')] = None
impulse_idAnnotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None
truncate_structured_labelsAnnotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None
labelsAnnotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples with a label within the given list of labels, given as a JSON string')] = None
filenameAnnotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples whose filename includes the given filename')] = None
max_lengthAnnotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples shorter than the given length, in milliseconds')] = None
min_lengthAnnotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples longer than the given length, in milliseconds')] = None
min_frequencyAnnotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples with higher frequency than given frequency, in hertz')] = None
max_frequencyAnnotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples with lower frequency than given frequency, in hertz')] = None
signature_validityAnnotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Include samples with either valid or invalid signatures')] = None
min_labelAnnotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples with a label >= this value')] = None
max_labelAnnotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples with a label < this value')] = None
searchAnnotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Search query')] = None
data_typeAnnotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Include only samples with a particular data type')] = None
min_idAnnotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Include only samples with an ID >= this value')] = None
max_idAnnotated[float | None, FieldInfo(annotation=NoneType, required=True, description='Include only samples with an ID < this value')] = None
metadataAnnotated[Annotated[str, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Filter samples by metadata key-value pairs, provided as a JSON string. Each item in the filter list is an object with the following properties
min_dateAnnotated[datetime.datetime | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples that where added after the date given')] = None
max_dateAnnotated[datetime.datetime | None, FieldInfo(annotation=NoneType, required=True, description='Only include samples that were added before the date given')] = None
**kwargs
Returns
edgeimpulse_api.models.classify_job_response_page.ClassifyJobResponsePage

​
get_sample_window_from_cache

edgeimpulse_api.api.classify_api.ClassifyApi.get_sample_window_from_cache(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	sample_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')],
	window_index: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample window index')],
	impulse_id: Annotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None,
	truncate_structured_labels: Annotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None,
	**kwargs
) ‑> edgeimpulse_api.models.get_sample_response.GetSampleResponse
Get a window of raw sample features from cache, after a live classification job has completed. Get raw sample features for a particular window. This is only available after a live classification job has completed and raw features have been cached.
Parameters
self
project_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')]
sample_idAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample ID')]
window_indexAnnotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Sample window index')]
impulse_idAnnotated[Annotated[int, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='Impulse ID. If this is unset then the default impulse is used.')] = None
truncate_structured_labelsAnnotated[Annotated[bool, Strict(strict=True)] | None, FieldInfo(annotation=NoneType, required=True, description='If true, only a slice of labels will be returned for samples with multiple labels.')] = None
**kwargs
Returns
edgeimpulse_api.models.get_sample_response.GetSampleResponse