{"ScriptPreparationCode":"function runPrediction(graphsArray, featuresArray)\r\n{\r\n let input = this.tf.tidy(() =\u003E\r\n {\r\n let featuresTensor = null;\r\n let graphsTensor = null;\r\n\r\n if (graphsArray \u0026\u0026 graphsArray.length \u003E 0)\r\n {\r\n let graphsTensors = [];\r\n for (let graph of graphsArray)\r\n {\r\n let graphTensor = this.tf.tensor(graph);\r\n let shape = graphTensor.shape;\r\n if (shape[0] \u003E GRAPH_SIZE)\r\n {\r\n graphTensor = graphTensor.slice(\r\n [0, 0], [GRAPH_SIZE, GRAPH_SIZE]\r\n );\r\n }\r\n else\r\n {\r\n graphTensor = graphTensor.pad([\r\n [0, GRAPH_SIZE - shape[0]],\r\n [0, GRAPH_SIZE - shape[1]]\r\n ]);\r\n }\r\n graphTensor = this._localPooling(graphTensor);\r\n graphsTensors.push(graphTensor);\r\n }\r\n graphsTensor = this.tf.stack(graphsTensors);\r\n }\r\n else\r\n {\r\n graphsTensor = this.tf.zeros([1, GRAPH_SIZE, GRAPH_SIZE]);\r\n }\r\n\r\n if (featuresArray \u0026\u0026 featuresArray.length \u003E 0)\r\n {\r\n let featuresTensors = [];\r\n for (let features of featuresArray)\r\n {\r\n let featuresPadded = null;\r\n if (features.length \u003C GRAPH_SIZE)\r\n {\r\n features = features.concat(new Array(GRAPH_SIZE).fill(0));\r\n }\r\n featuresPadded = features.slice(0, GRAPH_SIZE);\r\n\r\n featuresTensors.push(this.tf.oneHot(\r\n this.tf.tensor(\r\n featuresPadded,\r\n [1, GRAPH_SIZE], \u0022int32\u0022\r\n ),\r\n NUM_OF_TAGS)\r\n );\r\n }\r\n featuresTensor = this.tf.cast(\r\n this.tf.concat(featuresTensors), \u0022float32\u0022\r\n );\r\n }\r\n else\r\n {\r\n featuresTensor = this.tf.zeros([1, GRAPH_SIZE, NUM_OF_TAGS]);\r\n }\r\n return {graphsTensor, featuresTensor};\r\n });\r\n}","TestCases":[{"Name":"await runPrediction([], [])","Code":"1","IsDeferred":false},{"Name":"await runPrediction([1], [1])","Code":"2","IsDeferred":false}]}