How to use the @tensorflow/tfjs-node.concat function in @tensorflow/tfjs-node

return tf.tidy(() => {
    const xTrains = [];
    const yTrains = [];
    const xTests = [];
    const yTests = [];
    for (let i = 0; i < gestureClasses.length; ++i) {
      const [xTrain, yTrain, xTest, yTest] = convertToTensors(features[i], labels[i], 0.20);
      xTrains.push(xTrain);
      yTrains.push(yTrain);
      xTests.push(xTest);
      yTests.push(yTest);
    }

    const concatAxis = 0;
    return [
      tf.concat(xTrains, concatAxis), tf.concat(yTrains, concatAxis),
      tf.concat(xTests, concatAxis), tf.concat(yTests, concatAxis)
    ];
  })
}

const xTrains = [];
    const yTrains = [];
    const xTests = [];
    const yTests = [];
    for (let i = 0; i < gestureClasses.length; ++i) {
      const [xTrain, yTrain, xTest, yTest] = convertToTensors(features[i], labels[i], 0.20);
      xTrains.push(xTrain);
      yTrains.push(yTrain);
      xTests.push(xTest);
      yTests.push(yTest);
    }

    const concatAxis = 0;
    return [
      tf.concat(xTrains, concatAxis), tf.concat(yTrains, concatAxis),
      tf.concat(xTests, concatAxis), tf.concat(yTests, concatAxis)
    ];
  })
}

const xTrains = [];
    const yTrains = [];
    const xTests = [];
    const yTests = [];
    for (let i = 0; i < gestureClasses.length; ++i) {
      const [xTrain, yTrain, xTest, yTest] = convertToTensors(features[i], labels[i], 0.20);
      xTrains.push(xTrain);
      yTrains.push(yTrain);
      xTests.push(xTest);
      yTests.push(yTest);
    }

    const concatAxis = 0;
    return [
      tf.concat(xTrains, concatAxis), tf.concat(yTrains, concatAxis),
      tf.concat(xTests, concatAxis), tf.concat(yTests, concatAxis)
    ];
  })
}

const xTrains = [];
    const yTrains = [];
    const xTests = [];
    const yTests = [];
    for (let i = 0; i < gestureClasses.length; ++i) {
      const [xTrain, yTrain, xTest, yTest] = convertToTensors(features[i], labels[i], 0.20);
      xTrains.push(xTrain);
      yTrains.push(yTrain);
      xTests.push(xTest);
      yTests.push(yTest);
    }

    const concatAxis = 0;
    return [
      tf.concat(xTrains, concatAxis), tf.concat(yTrains, concatAxis),
      tf.concat(xTests, concatAxis), tf.concat(yTests, concatAxis)
    ];
  })
}

return tf.tidy(() => {
    const xTrains = [];
    const yTrains = [];
    const xTests = [];
    const yTests = [];
    for (let i = 0; i < gestureClasses.length; ++i) {
      const [xTrain, yTrain, xTest, yTest] = convertToTensors(features[i], labels[i], 0.20);
      xTrains.push(xTrain);
      yTrains.push(yTrain);
      xTests.push(xTest);
      yTests.push(yTest);
    }

    const concatAxis = 0;
    return [
      tf.concat(xTrains, concatAxis), tf.concat(yTrains, concatAxis),
      tf.concat(xTests, concatAxis), tf.concat(yTests, concatAxis)
    ];
  })
}

const xTrains = [];
    const yTrains = [];
    const xTests = [];
    const yTests = [];
    for (let i = 0; i < gestureClasses.length; ++i) {
      const [xTrain, yTrain, xTest, yTest] = convertToTensors(features[i], labels[i], 0.20);
      xTrains.push(xTrain);
      yTrains.push(yTrain);
      xTests.push(xTest);
      yTests.push(yTest);
    }

    const concatAxis = 0;
    return [
      tf.concat(xTrains, concatAxis), tf.concat(yTrains, concatAxis),
      tf.concat(xTests, concatAxis), tf.concat(yTests, concatAxis)
    ];
  })
}

const centroid = tf.tidy(() => {
        const allEmbTensor = tf.concat(allEmbeddings);

        if (allEmbTensor.shape[0] !== samples.length) {
            throw new Error(`Some embeddings are missing: allEmbTensor.shape[0] !== samples.length: ${allEmbTensor.shape[0]} !== ${samples.length}`);
        }
        let centroid = allEmbTensor.mean(axis = 0);
        if (NORMALIZE_CENTROID) {
            centroid = normalize1d(centroid);
        }
        return centroid.arraySync();
    });
    allEmbeddings.forEach(emb => emb.dispose());