import {z} from 'zod'

export type GetApiApiProjectIdImpulsesDetailedArgs = {projectId: number};

export type GetApiApiProjectIdImpulsesDetailedOptions = {baseUrl?: string | undefined, ApiKeyAuthentication?: string | undefined, fetch?: typeof fetch | undefined};

export const inputBlockType = z.enum(["time-series", "image", "features"]);

export const imageInputResizeMode = z.enum(["squash", "fit-short", "fit-long", "crop"]);

export const impulseInputBlockDatasetSubset = z.object({includePercentage: z.number(), seed: z.number().int().optional()});

export const impulseInputBlock = z.object({id: z.number().int().gte(1), type: inputBlockType, name: z.string(), title: z.string(), axes: z.array(z.string()).optional(), windowSizeMs: z.number().int().optional(), windowIncreaseMs: z.number().int().optional(), frequencyHz: z.number().optional(), classificationWindowIncreaseMs: z.number().int().optional(), padZeros: z.boolean().optional(), labelingMethodMultiLabel: z.object({type: z.enum(["end-of-window", "anywhere-in-window", "anywhere-in-window-min-percentage", "majority-in-window"]), labels: z.array(z.string()).optional(), minPercentage: z.number().optional()}).optional(), imageWidth: z.number().int().optional(), imageHeight: z.number().int().optional(), resizeMode: imageInputResizeMode.optional(), resizeMethod: z.enum(["lanczos3", "nearest"]).optional(), cropAnchor: z.enum(["top-left", "top-center", "top-right", "middle-left", "middle-center", "middle-right", "bottom-left", "bottom-center", "bottom-right"]).optional(), createdBy: z.string().optional(), createdAt: z.string().optional(), datasetSubset: impulseInputBlockDatasetSubset.optional()});

export const impulseDspBlock = z.object({id: z.number().int().gte(1), type: z.string(), name: z.string(), axes: z.array(z.string()), title: z.string(), valuesPerAxis: z.number().int().optional(), input: z.number().int().optional(), createdBy: z.string().optional(), createdAt: z.string().optional(), implementationVersion: z.number().int(), organization: z.object({id: z.number().int(), dspId: z.number().int()}).optional(), customUrl: z.string().optional(), namedAxes: z.array(z.object({name: z.string(), description: z.string().optional(), required: z.boolean().optional(), selectedAxis: z.string().optional()})).optional()});

export const learnBlockType = z.enum(["anomaly", "anomaly-gmm", "keras", "keras-transfer-image", "keras-transfer-kws", "keras-object-detection", "keras-regression", "keras-akida", "keras-akida-transfer-image", "keras-akida-object-detection", "keras-visual-anomaly", "keras-freeform", "keras-self-contained-freeform", "keras-anomaly", "vlm"]);

export const impulseSpecificGpuTrainingProcessor = z.enum(["cpu", "gpu"]);

export const impulseLearnBlock = z.object({id: z.number().int().gte(1), type: learnBlockType, name: z.string(), dsp: z.array(z.number().int()), title: z.string(), createdBy: z.string().optional(), createdAt: z.string().optional(), trainingProcessor: impulseSpecificGpuTrainingProcessor.optional()});

export const postProcessingBlockType = z.enum(["object-tracking", "sort-object-tracking"]);

export const impulsePostProcessingBlock = z.object({id: z.number().int().gte(1), type: postProcessingBlockType, name: z.string(), title: z.string(), createdBy: z.string().optional(), createdAt: z.string().optional(), implementationVersion: z.number().int()});

export const impulseType = z.enum(["default", "BYOM", "VLM", "no_training_data"]);

export const impulse = z.object({id: z.number().int(), name: z.string(), inputBlocks: z.array(impulseInputBlock), dspBlocks: z.array(impulseDspBlock), learnBlocks: z.array(impulseLearnBlock), postProcessingBlocks: z.array(impulsePostProcessingBlock), type: impulseType, datasetVersionId: z.number().int().optional()});

export const detailedImpulseMetricFilteringType = z.object({type: z.enum(["numeric", "string", "select", "boolean", "list"]), options: z.array(z.string())});

export const detailedImpulseMetricCategory = z.enum(["impulseMetrics", "inputBlockConfig", "dspBlockConfig", "learnBlockConfig", "learnBlockMetrics", "postProcessingBlockConfig"]);

export const detailedImpulseMetric = z.object({name: z.string(), type: z.enum(["core", "additional"]), filteringType: detailedImpulseMetricFilteringType.optional(), category: detailedImpulseMetricCategory, description: z.string(), value: z.union([z.string(), z.boolean()]), title: z.string().optional(), valueForSorting: z.number().int().optional(), valueHint: z.string().optional()});

export const dspPerformance = z.object({latency: z.number().int(), ram: z.number().int(), customDspString: z.string().optional()});

export const dSPNormalizeData = z.enum(["none", "normalize-channel-standard-scaler"]);

export const dSPInfo = z.object({id: z.number().int(), name: z.string(), windowLength: z.number().int(), type: z.string(), classes: z.array(z.string()), classesPerKey: z.record(z.string(), z.array(z.string())).optional(), features: z.object({generated: z.boolean(), count: z.number().int().optional(), labels: z.array(z.string()).optional(), classes: z.array(z.string()).optional()}), expectedWindowCount: z.number().int(), inputAxes: z.array(z.string()), performance: dspPerformance.optional(), canCalculateFeatureImportance: z.boolean(), calculateFeatureImportance: z.boolean(), canNormalizeData: z.boolean(), normalizeData: dSPNormalizeData, hasAutoTune: z.boolean().optional(), minimumVersionForAutotune: z.number().optional(), hasAutotunerResults: z.boolean().optional(), usesState: z.boolean().optional()});

export const dSPGroupItem = z.object({name: z.string(), value: z.string().optional(), defaultValue: z.string(), type: z.string(), help: z.string().optional(), param: z.string(), selectOptions: z.array(z.object({value: z.string().optional(), selected: z.boolean().optional(), optionLabel: z.string().optional(), priority: z.number().optional(), romEstimate: z.number().optional(), needsOps: z.array(z.string()).optional(), needsFeatures: z.array(z.string()).optional()})).optional(), readonly: z.boolean(), shouldShow: z.boolean(), showIf: z.object({parameter: z.string(), operator: z.enum(["eq", "neq"]), value: z.string()}).optional(), invalidText: z.string().optional(), section: z.enum(["advanced", "augmentation", "modelProfiling"]).optional(), multiline: z.boolean().optional(), required: z.boolean(), hint: z.string().optional(), placeholder: z.string().optional(), showClickToSet: z.boolean(), valid: z.array(z.object({})).optional(), items: z.object({}).optional(), properties: z.object({}).optional(), minVal: z.number().optional(), maxVal: z.number().optional()});

export const dSPGroup = z.object({group: z.string(), items: z.array(dSPGroupItem)});

export const dSPConfig = z.object({dsp: dSPInfo, config: z.array(dSPGroup).optional(), configError: z.string().optional()});

export const dSPMetadata = z.object({created: z.string(), generated: z.boolean(), dspConfig: z.record(z.string(), z.string()), labels: z.array(z.string()), featureLabels: z.array(z.string()).optional(), windowCount: z.number().int(), featureCount: z.number().int(), includedSamples: z.array(z.object({id: z.number().int(), windowCount: z.number().int()})), windowSizeMs: z.number().int(), windowIncreaseMs: z.number().int(), padZeros: z.boolean(), frequency: z.number(), outputConfig: z.object({type: z.enum(["image", "spectrogram", "flat"]), shape: z.object({width: z.number().int(), height: z.number().int().optional(), channels: z.number().int().optional(), frames: z.number().int().optional()})}), fftUsed: z.array(z.number().int()).optional(), resamplingAlgorithmVersion: z.number().optional(), featureExplorerJobId: z.number().int().optional(), featureExplorerJobFailed: z.boolean().optional(), featureImportanceJobId: z.number().int().optional(), featureImportanceJobFailed: z.boolean().optional()});

export const dependencyData = z.object({classes: z.array(z.string()), classesPerKey: z.record(z.string(), z.array(z.string())).optional(), blockNames: z.array(z.string()), featureCount: z.number().int(), sampleCount: z.number().int(), outputClasses: z.array(z.string())});

export const kerasModelTypeEnum = z.enum(["int8", "float32", "akida", "requiresRetrain"]);

export const kerasVisualLayerType = z.enum(["dense", "conv1d", "conv2d", "reshape", "flatten", "dropout", "batchNormalization", "transfer_mobilenetv2_a35", "transfer_mobilenetv2_a1", "transfer_mobilenetv2_a05", "transfer_mobilenetv2_160_a1", "transfer_mobilenetv2_160_a75", "transfer_mobilenetv2_160_a5", "transfer_mobilenetv2_160_a35", "transfer_mobilenetv1_a25_d100", "transfer_mobilenetv1_a2_d100", "transfer_mobilenetv1_a1_d100", "transfer_kws_mobilenetv1_a1_d100", "transfer_kws_mobilenetv2_a35_d100", "transfer_kws_syntiant_ndp10x", "transfer_kws_conv2d_tiny", "object_ssd_mobilenet_v2_fpnlite_320x320", "fomo_mobilenet_v2_a01", "fomo_mobilenet_v2_a35", "transfer_organization", "transfer_akidanet_imagenet_160_a100", "transfer_akidanet_imagenet_160_a50", "transfer_akidanet_imagenet_160_a25", "transfer_akidanet_imagenet_224_a100", "transfer_akidanet_imagenet_224_a50", "transfer_akidanet_imagenet_224_a25", "fomo_akidanet_a50", "fomo_ad_gmm", "fomo_ad_patchcore"]);

export const kerasVisualLayer = z.object({type: kerasVisualLayerType, neurons: z.number().int().optional(), kernelSize: z.number().int().optional(), dropoutRate: z.number().optional(), columns: z.number().int().optional(), stack: z.number().int().optional(), enabled: z.boolean().optional(), organizationModelId: z.number().int().optional()});

export const augmentationPolicyImageEnum = z.enum(["none", "all"]);

export const augmentationPolicySpectrogram = z.object({enabled: z.boolean(), warping: z.boolean().optional(), freqMasking: z.enum(["none", "low", "high"]).optional(), timeMasking: z.enum(["none", "low", "high"]).optional(), gaussianNoise: z.enum(["none", "low", "high"]).optional()});

export const blockType = z.enum(["official", "personal", "enterprise", "community"]);

export const blockDisplayCategory = z.enum(["classical", "tao", "developer-preview"]);

export const transferLearningModel = z.object({name: z.string(), shortName: z.string(), abbreviatedName: z.string().optional(), description: z.string(), hasNeurons: z.boolean(), hasDropout: z.boolean(), defaultNeurons: z.number().int().optional(), defaultDropout: z.number().optional(), defaultLearningRate: z.number().optional(), defaultTrainingCycles: z.number().optional(), hasImageAugmentation: z.boolean().optional(), type: kerasVisualLayerType, learnBlockType: learnBlockType, organizationModelId: z.number().int().optional(), implementationVersion: z.number().int().optional(), repositoryUrl: z.string().optional(), customBlockRef: z.string().optional(), author: z.string(), blockType: blockType, customParameters: z.array(dSPGroupItem).optional(), displayCategory: blockDisplayCategory.optional(), blockNoLongerAvailable: z.object({reasonMarkdown: z.string(), reasonHtml: z.string()}).optional(), hasExpertMode: z.boolean()});

export const akidaEdgeLearningConfig = z.object({enabled: z.boolean(), additionalClasses: z.number().optional(), neuronsPerClass: z.number().optional()});

export const anomalyCapacity = z.enum(["low", "medium", "high"]);

export const kerasModelVariantEnum = z.enum(["int8", "float32", "akida"]);

export const blockParamsVisualAnomalyPatchcore = z.object({backbone: z.string().optional(), numLayers: z.number().int().optional(), poolSize: z.number().int().optional(), samplingRatio: z.number().optional(), numNearestNeighbors: z.number().int().optional()});

export const blockParamsVisualAnomalyGmm = z.object({backbone: z.string().optional()});

export const blockParameters = z.union([blockParamsVisualAnomalyPatchcore, blockParamsVisualAnomalyGmm]);

export const blockThreshold = z.object({key: z.string(), description: z.string(), helpText: z.string(), suggestedValue: z.number().optional(), suggestedValueText: z.string().optional(), value: z.union([z.number(), z.string()]), dropdownOptions: z.array(z.object({description: z.string(), value: z.string()})).optional()});

export const kFoldsValidationConfig = z.object({numFolds: z.number(), includeFinalTrainingRun: z.boolean().optional(), extraEpochs: z.number().optional()});

export const kerasConfig = z.object({dependencies: dependencyData, trained: z.boolean(), name: z.string(), type: learnBlockType.optional(), script: z.string(), minimumConfidenceRating: z.number(), selectedModelType: kerasModelTypeEnum, mode: z.enum(["visual", "expert"]), visualLayers: z.array(kerasVisualLayer), trainingCycles: z.number().int(), learningRate: z.number(), batchSize: z.number().int().optional(), defaultBatchSize: z.number().int(), shape: z.string(), trainTestSplit: z.number().optional(), autoClassWeights: z.boolean().optional(), useLearnedOptimizer: z.boolean().optional(), augmentationPolicyImage: augmentationPolicyImageEnum, augmentationPolicySpectrogram: augmentationPolicySpectrogram.optional(), transferLearningModels: z.array(transferLearningModel), profileInt8: z.boolean(), skipEmbeddingsAndMemory: z.boolean(), akidaEdgeLearningConfig: akidaEdgeLearningConfig.optional(), customValidationMetadataKey: z.string().optional(), showAdvancedTrainingSettings: z.boolean(), showAugmentationTrainingSettings: z.boolean(), customParameters: z.record(z.string(), z.string().nullable()).optional(), anomalyCapacity: anomalyCapacity.optional(), lastShownModelVariant: kerasModelVariantEnum.optional(), blockParameters: blockParameters.optional(), thresholds: z.array(blockThreshold), kFoldsValidationConfig: kFoldsValidationConfig.optional(), isMultiHead: z.boolean().optional()});

export const kerasModelLayer = z.object({input: z.object({shape: z.number().int(), name: z.string(), type: z.string()}), output: z.object({shape: z.number().int(), name: z.string(), type: z.string()})});

export const kerasCustomMetric = z.object({name: z.string(), value: z.string()});

export const boundingBoxWithScore = z.object({label: z.string(), x: z.number(), y: z.number(), width: z.number(), height: z.number(), score: z.number()});

export const modelPrediction = z.object({sampleId: z.number().int(), startMs: z.number(), endMs: z.number(), label: z.string().optional(), prediction: z.string(), predictionCorrect: z.boolean().optional(), expectedAnomalyOutcome: z.string().optional(), f1Score: z.number().optional(), anomalyScores: z.array(z.array(z.number())).optional(), boundingBoxes: z.array(boundingBoxWithScore).optional(), labelMapPredictions: z.record(z.string(), z.string()).optional()});

export const additionalMetric = z.object({name: z.string(), value: z.string(), fullPrecisionValue: z.number(), tooltipText: z.string().optional(), link: z.string().optional()});

export const kerasModelMetadataMetrics = z.object({type: kerasModelTypeEnum, loss: z.number(), accuracy: z.number().optional(), confusionMatrix: z.array(z.array(z.number())), report: z.object({}), onDevicePerformance: z.array(z.object({mcu: z.string(), name: z.string(), isDefault: z.boolean(), latency: z.number(), tflite: z.object({ramRequired: z.number().int(), romRequired: z.number().int(), arenaSize: z.number().int(), modelSize: z.number().int()}), eon: z.object({ramRequired: z.number().int(), romRequired: z.number().int(), arenaSize: z.number().int(), modelSize: z.number().int()}), eon_ram_optimized: z.object({ramRequired: z.number().int(), romRequired: z.number().int(), arenaSize: z.number().int(), modelSize: z.number().int()}).optional(), customMetrics: z.array(kerasCustomMetric).optional(), hasPerformance: z.boolean(), profilingError: z.string().optional()})), predictions: z.array(modelPrediction).optional(), visualization: z.enum(["featureExplorer", "dataExplorer", "none"]), isSupportedOnMcu: z.boolean(), mcuSupportError: z.string().optional(), profilingJobId: z.number().int().optional(), profilingJobFailed: z.boolean().optional(), additionalMetrics: z.array(additionalMetric)});

export const kerasModelMode = z.enum(["classification", "regression", "object-detection", "visual-anomaly", "anomaly-gmm", "freeform", "anomaly"]);

export const objectDetectionLastLayer = z.enum(["mobilenet-ssd", "fomo", "yolov2-akida", "yolov5", "yolov5v5-drpai", "yolox", "yolov7", "yolo-pro", "tao-retinanet", "tao-ssd", "tao-yolov3", "tao-yolov4", "yolov11", "yolov11-abs", "paddleocr-detector", "qc-face-det-lite", "qc-yolox"]);

export const imageInputScaling = z.enum(["0..1", "-1..1", "-128..127", "0..255", "torch", "bgr-subtract-imagenet-mean"]);

export const kerasModelMetadataGraphSeries = z.object({title: z.string(), values: z.array(z.number())});

export const kerasModelMetadataGraph = z.object({title: z.string(), xLabel: z.string().optional(), yLabel: z.string().optional(), description: z.string().optional(), hideInUI: z.boolean().optional(), data: z.array(kerasModelMetadataGraphSeries)});

export const tensorboardGraphs = z.array(kerasModelMetadataGraph);

export const foldModelMetadata = z.object({created: z.string(), layers: z.array(kerasModelLayer), classNames: z.array(z.string()), labels: z.array(z.string()), availableModelTypes: z.array(kerasModelTypeEnum), recommendedModelType: kerasModelTypeEnum, modelValidationMetrics: z.array(kerasModelMetadataMetrics), hasTrainedModel: z.boolean(), mode: kerasModelMode, objectDetectionLastLayer: objectDetectionLastLayer.optional(), imageInputScaling: imageInputScaling, thresholds: z.array(blockThreshold), tensorboardGraphs: tensorboardGraphs.optional(), foldId: z.number().int().gte(0), foldName: z.string()});

export const kFoldsMetadata = z.object({folds: z.array(foldModelMetadata), hasFinalRun: z.boolean().optional(), persistedFoldId: z.number().int(), persistedFoldName: z.string(), persistedFoldMetricType: kerasModelTypeEnum.optional()});

export const kerasModelMetadata = z.object({created: z.string(), layers: z.array(kerasModelLayer), classNames: z.array(z.string()), labels: z.array(z.string()), availableModelTypes: z.array(kerasModelTypeEnum), recommendedModelType: kerasModelTypeEnum, modelValidationMetrics: z.array(kerasModelMetadataMetrics), hasTrainedModel: z.boolean(), mode: kerasModelMode, objectDetectionLastLayer: objectDetectionLastLayer.optional(), imageInputScaling: imageInputScaling, thresholds: z.array(blockThreshold), tensorboardGraphs: tensorboardGraphs.optional(), kFoldsMetadata: kFoldsMetadata.optional()});

export const anomalyConfig = z.object({dependencies: dependencyData, name: z.string(), axes: z.array(z.object({ix: z.number().int(), label: z.string(), selected: z.boolean(), favourite: z.boolean()})), trained: z.boolean(), clusterCount: z.number().int().optional(), selectedAxes: z.array(z.number().int()), minimumConfidenceRating: z.number(), thresholds: z.array(blockThreshold)});

export const anomalyModelMetadata = z.object({created: z.string(), scale: z.array(z.number()), mean: z.array(z.number()), clusters: z.array(z.object({center: z.array(z.number()), maxError: z.number()})), axes: z.array(z.number().int()), defaultMinimumConfidenceRating: z.number(), availableModelTypes: z.array(kerasModelTypeEnum).optional(), recommendedModelType: kerasModelTypeEnum.optional(), modelValidationMetrics: z.array(kerasModelMetadataMetrics).optional(), hasTrainedModel: z.boolean().optional(), thresholds: z.array(blockThreshold)});

export const anomalyGmmMetadata = z.object({means: z.array(z.array(z.number())), covariances: z.array(z.array(z.array(z.number()))), weights: z.array(z.number())});

export const postProcessingConfig = z.object({enabled: z.boolean(), parameters: z.array(dSPGroupItem), type: postProcessingBlockType});

export const createdUpdatedByUser = z.object({id: z.number().int(), name: z.string(), username: z.string(), photo: z.string().optional()});

export const datasetVersionType = z.enum(["auto_save", "restore", "manual"]);

export const datasetVersionSummary = z.object({rawEventCountInWindow: z.number(), sampleChangesCount: z.number(), actionCounts: z.object({insert: z.number().optional(), update: z.number().optional(), delete: z.number().optional()})});

export const actorUser = z.object({id: z.number().int(), name: z.string(), username: z.string(), photo: z.string().optional(), type: z.literal("user")});

export const actorOAuthClient = z.object({type: z.literal("oauthClient"), id: z.number().int(), clientId: z.string(), name: z.string(), logoUrl: z.string().optional(), user: createdUpdatedByUser.optional()});

export const actorProjectApiKey = z.object({type: z.literal("projectApiKey"), name: z.string(), created: z.string(), createdByUser: createdUpdatedByUser.optional()});

export const datasetVersion = z.object({id: z.number().int(), created: z.string(), versionIndex: z.number().int(), description: z.string().nullable(), parentVersionId: z.number().int().nullable(), restoredFromDatasetVersionId: z.number().int().nullable().optional(), type: datasetVersionType, summary: datasetVersionSummary, windowStart: z.string(), windowEnd: z.string(), actors: z.array(z.discriminatedUnion("type", [actorUser, actorOAuthClient, actorProjectApiKey]))});

export const detailedImpulse = z.object({impulse: impulse, metrics: z.array(detailedImpulseMetric), dspBlockConfigs: z.array(z.object({blockId: z.number().int(), config: dSPConfig, metadata: dSPMetadata.optional()})), learnBlockKerasConfigs: z.array(z.object({blockId: z.number().int(), config: kerasConfig, metadata: kerasModelMetadata.optional()})), learnBlockAnomalyConfigs: z.array(z.object({blockId: z.number().int(), config: anomalyConfig, metadata: anomalyModelMetadata.optional(), gmmMetadata: anomalyGmmMetadata.optional()})), postProcessingBlockConfigs: z.array(z.object({blockId: z.number().int(), config: postProcessingConfig})), pretrainedModelInfo: z.object({fileName: z.string()}).optional(), isStale: z.boolean(), configured: z.boolean(), complete: z.boolean(), tags: z.array(z.string()), createdFromTunerTrialId: z.number().optional(), createdByUser: createdUpdatedByUser.optional(), datasetVersion: datasetVersion.optional()});

export const getAllDetailedImpulsesResponse = z.object({success: z.boolean(), error: z.string().optional(), impulses: z.array(detailedImpulse), metricKeysByCategory: z.array(z.object({category: detailedImpulseMetricCategory, metricKeys: z.array(z.object({name: z.string(), description: z.string(), type: z.enum(["core", "additional"]), filteringType: detailedImpulseMetricFilteringType.optional(), showInTable: z.boolean()}))})), extraTableColumns: z.array(z.string())});

export const validateResponse = async <Output>(
  schema: {
    readonly '~standard': {
      readonly validate: (value: unknown) => ({ readonly value: Output; readonly issues?: undefined } | { readonly issues: ReadonlyArray<{ readonly message: string; readonly path?: ReadonlyArray<PropertyKey | { readonly key: PropertyKey }> | undefined }> }) | Promise<{ readonly value: Output; readonly issues?: undefined } | { readonly issues: ReadonlyArray<{ readonly message: string; readonly path?: ReadonlyArray<PropertyKey | { readonly key: PropertyKey }> | undefined }> }>
    }
  },
  value: unknown
): Promise<Output> => {
  const result = await schema['~standard'].validate(value)

  if (result.issues !== undefined) {
    const detail = result.issues
      .map(({ message, path }) =>
        path?.length
          ? `${path.map((segment) => String(typeof segment === 'object' ? segment.key : segment)).join('.')}: ${message}`
          : message
      )
      .join('; ')

    throw new Error(`Response does not match the schema: ${detail}`, { cause: { issues: result.issues } })
  }

  return result.value
};

/**
 * Get all impulses (incl. metrics)
 * 
 * GET /api/{projectId}/impulses-detailed
 * 
 * Also accepts auth: JWTAuthentication, JWTHttpHeaderAuthentication, OAuth2
 */
export const getApiApiProjectIdImpulsesDetailed = async (args: GetApiApiProjectIdImpulsesDetailedArgs, options?: GetApiApiProjectIdImpulsesDetailedOptions) => {
const {baseUrl = "https://studio.edgeimpulse.com/v1", fetch: fetchFn = fetch, ApiKeyAuthentication} = options ?? {}

const query = new URLSearchParams()
const headers = new Headers()
headers.set('accept', "application/json")

if (ApiKeyAuthentication !== undefined) headers.set("x-api-key", ApiKeyAuthentication)

const url = `${baseUrl.replace(/\/+$/, '')}/api/${encodeURIComponent(String(args.projectId))}/impulses-detailed${query.size > 0 ? `?${query}` : ''}`

const res = await fetchFn(url, {method: "GET", headers})

if (!res.ok) throw new Error(`Request failed with status ${res.status}`, {cause: {status: res.status, body: await res.text()}})

return validateResponse(getAllDetailedImpulsesResponse, await res.json())
};