lint fix

tfjs-core/src/backends/backend_util.ts

@@ -75,7 +75,7 @@ export function castTensor<T extends Tensor>(
     const real = backend.real(x);
     const result = cast(real, dtype);
     real.dispose();
-    return result as T;
+    return result;
   }
   if (dtype === 'int32') {
     return backend.int(x);

tfjs-core/src/ops/batchnorm.ts

@@ -119,12 +119,15 @@ function as1DOr4D(x: Tensor): Tensor4D|Tensor1D {
     return null;
   }
   if (x.rank === 0) {
+    // tslint:disable-next-line:no-unnecessary-type-assertion
     return reshape(x, [x.size]) as Tensor1D;
   } else if (x.rank === 1) {
     return x as Tensor1D;
   } else if (x.rank === 2) {
+    // tslint:disable-next-line:no-unnecessary-type-assertion
     return reshape(x, [1, 1, x.shape[0], x.shape[1]]) as Tensor4D;
   } else if (x.rank === 3) {
+    // tslint:disable-next-line:no-unnecessary-type-assertion
     return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]) as Tensor4D;
   }
   return x as Tensor4D;

tfjs-core/src/ops/eye.ts

@@ -24,7 +24,6 @@ import {op} from './operation';
 import {reshape} from './reshape';
 import {tile} from './tile';
 
-
 /**
  * Create an identity matrix.
  *
@@ -54,17 +53,19 @@ function eye_(
   for (let i = 0; i < n; ++i) {
     buff.set(1, i, i);
   }
-  const out = reshape(buff.toTensor(), [numRows, numColumns]) as Tensor2D;
+  const out: Tensor2D = reshape(buff.toTensor(), [numRows, numColumns]);
   if (batchShape == null) {
     return out;
   } else {
     if (batchShape.length === 1) {
       return tile(expandDims(out, 0), [batchShape[0], 1, 1]) as Tensor2D;
     } else if (batchShape.length === 2) {
+      // tslint:disable-next-line:no-unnecessary-type-assertion
       return tile(
                  expandDims(expandDims(out, 0), 0),
                  [batchShape[0], batchShape[1], 1, 1]) as Tensor2D;
     } else if (batchShape.length === 3) {
+      // tslint:disable-next-line:no-unnecessary-type-assertion
       return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
                batchShape[0], batchShape[1], batchShape[2], 1, 1
              ]) as Tensor2D;

tfjs-core/src/ops/multinomial.ts

@@ -61,6 +61,7 @@ function multinomial_(
   const res = ENGINE.runKernelFunc(
       backend => backend.multinomial(logits2D, normalized, numSamples, seed),
       {logits2D});
+  // tslint:disable-next-line:no-unnecessary-type-assertion
   return origRank === 1 ? reshape(res, [res.size]) as Tensor1D : res;
 }