Method standardizeUserDataXY

tfjs-layers/src/engine/training.ts:1145–1184

(
      x: Tensor|Tensor[]|{[inputName: string]: Tensor},
      y: Tensor|Tensor[]|{[inputName: string]: Tensor}, checkBatchAxis = true,
      batchSize?: number)

  }

  protected standardizeUserDataXY(
      x: Tensor|Tensor[]|{[inputName: string]: Tensor},
      y: Tensor|Tensor[]|{[inputName: string]: Tensor}, checkBatchAxis = true,
      batchSize?: number): [Tensor[], Tensor[]] {
    // TODO(cais): Add sampleWeight, classWeight
    if (this.optimizer_ == null) {
      throw new RuntimeError(
          'You must compile a model before training/testing. Use ' +
          'LayersModel.compile(modelCompileArgs).');
    }
    const outputShapes: Shape[] = [];
    for (let i = 0; i < this.feedOutputShapes.length; ++i) {
      const outputShape = this.feedOutputShapes[i];
      const lossFn = this.feedLossFns[i];
      if (lossFn === losses.sparseCategoricalCrossentropy) {
        outputShapes.push(
            outputShape.slice(0, outputShape.length - 1).concat([1]));
      } else {
        // Porting Note: Because of strong typing `lossFn` must be a function.
        outputShapes.push(outputShape);
      }
    }
    x = standardizeInputData(
        x, this.feedInputNames, this.feedInputShapes, false, 'input');
    y = standardizeInputData(
        y, this.feedOutputNames, outputShapes, false, 'target');
    // TODO(cais): Standardize sampleWeights & classWeights.
    checkArrayLengths(x, y, null);
    // TODO(cais): Check sampleWeights as well.
    checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);
    if (this.stateful && batchSize != null && batchSize > 0) {
      if (x[0].shape[0] % batchSize !== 0) {
        throw new ValueError(
            `In a stateful network, you should only pass inputs with a ` +
            `number of samples that is divisible by the batch size ` +
            `${batchSize}. Found: ${x[0].shape[0]} sample(s).`);
      }
    }
    return [x, y];
  }

  protected async standardizeUserData(
      x: Tensor|Tensor[]|{[inputName: string]: Tensor},