[tfjs-core] Upstream the closure fixes

tfjs-core/src/backends/webgl/backend_webgl.ts

@@ -487,9 +487,10 @@ export class MathBackendWebGL implements KernelBackend {
     // Download the values from the GPU.
     let vals: Float32Array;
     if (dtype === 'complex64') {
-      const ps =
-          Promise.all([complexTensors.real.data(), complexTensors.imag.data()]);
-      const [realValues, imagValues] = await ps;
+      const ps = await Promise.all(
+          [complexTensors.real.data(), complexTensors.imag.data()]);
+      const realValues = ps[0];
+      const imagValues = ps[1];
       vals = mergeRealAndImagArrays(
           realValues as Float32Array, imagValues as Float32Array);
     } else if (buffer == null) {

tfjs-core/src/backends/webgl/conv_backprop_gpu_depthwise.ts

@@ -43,7 +43,7 @@ export class DepthwiseConv2DDerFilterProgram implements GPGPUProgram {
 
         float dotProd = 0.0;
 
-        // TODO: Vec4 over the batch size
+        // TO DO: Vec4 over the batch size
         for (int b = 0; b < ${convInfo.batchSize}; b++) {
           for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
             int xR = wR + yR * ${strideHeight} - ${padTop};
@@ -122,7 +122,7 @@ export class DepthwiseConv2DDerInputProgram implements GPGPUProgram {
 
             int wCPerm = ${filterWidth} - 1 - wC;
 
-            // TODO: Vec4 over the channelMul
+            // TO DO: Vec4 over the channelMul
             for (int dm = 0; dm < ${channelMul}; dm++) {
               int d2 = d1 * ${channelMul} + dm;
               float xValue = getDy(batch, idyR, idyC, d2);

tfjs-core/src/backends/webgl/conv_gpu_depthwise.ts

@@ -56,7 +56,7 @@ export class DepthwiseConv2DProgram implements GPGPUProgram {
         // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
-        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.
+        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
         for (int wR = 0; wR < ${filterHeight}; wR++) {
           int xR = xRCorner + wR * ${dilationHeight};
 

tfjs-core/src/ops/browser.ts

@@ -97,8 +97,9 @@ export async function toPixels(
   const data = await $img.data();
   const minTensor = $img.min();
   const maxTensor = $img.max();
-  const [minVals, maxVals] =
-      await Promise.all([minTensor.data(), maxTensor.data()]);
+  const vals = await Promise.all([minTensor.data(), maxTensor.data()]);
+  const minVals = vals[0];
+  const maxVals = vals[1];
   const min = minVals[0];
   const max = maxVals[0];
   minTensor.dispose();