diff --git a/tfjs-layers/src/base_callbacks.ts b/tfjs-layers/src/base_callbacks.ts
index 50eea0c8a56..e79cbf11eb2 100644
--- a/tfjs-layers/src/base_callbacks.ts
+++ b/tfjs-layers/src/base_callbacks.ts
@@ -488,7 +488,8 @@ export function standardizeCallbacks(
   }
   // Convert custom callback configs to custom callback objects.
   const callbackConfigs =
-      generic_utils.toList(callbacks) as CustomCallbackArgs[];
+      generic_utils.toList<BaseCallback | CustomCallbackArgs>(
+        callbacks) as CustomCallbackArgs[];
   return callbackConfigs.map(
       callbackConfig => new CustomCallback(callbackConfig, yieldEvery));
 }
diff --git a/tfjs-layers/src/engine/topology.ts b/tfjs-layers/src/engine/topology.ts
index 85ba63ccd66..ffa581560ba 100644
--- a/tfjs-layers/src/engine/topology.ts
+++ b/tfjs-layers/src/engine/topology.ts
@@ -751,19 +751,19 @@ export abstract class Layer extends serialization.Serializable {
    */
   protected assertInputCompatibility(inputs: Tensor|Tensor[]|SymbolicTensor|
                                      SymbolicTensor[]): void {
-    inputs = generic_utils.toList(inputs);
+    const inputsList = generic_utils.toList(inputs);
     if (this.inputSpec == null || this.inputSpec.length === 0) {
       return;
     }
     const inputSpec = generic_utils.toList(this.inputSpec);
-    if (inputs.length !== inputSpec.length) {
+    if (inputsList.length !== inputSpec.length) {
       throw new ValueError(
           `Layer ${this.name} expects ${inputSpec.length} inputs, ` +
-          `but it received ${inputs.length} input tensors. ` +
+          `but it received ${inputsList.length} input tensors. ` +
           `Input received: ${inputs}`);
     }
-    for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {
-      const x = inputs[inputIndex];
+    for (let inputIndex = 0; inputIndex < inputsList.length; inputIndex++) {
+      const x = inputsList[inputIndex];
       const spec: InputSpec = inputSpec[inputIndex];
       if (spec == null) {
         continue;
@@ -954,20 +954,8 @@ export abstract class Layer extends serialization.Serializable {
     // Ensure inputs are all the same type.
     const inputsList = generic_utils.toList(inputs);
 
-    let allAreSymbolic = true;
-    for (const input of inputsList) {
-      if (!(input instanceof SymbolicTensor)) {
-        allAreSymbolic = false;
-        break;
-      }
-    }
-    let noneAreSymbolic = true;
-    for (const input of inputsList) {
-      if (input instanceof SymbolicTensor) {
-        noneAreSymbolic = false;
-        break;
-      }
-    }
+    const allAreSymbolic = checkAllSymbolic(inputs);
+    const noneAreSymbolic = checkNoneSymbolic(inputs);
 
     if (allAreSymbolic === noneAreSymbolic) {
       throw new ValueError(
@@ -1017,31 +1005,12 @@ export abstract class Layer extends serialization.Serializable {
 
       // Actually call the layer, collecting output(s), mask(s), and shape(s).
       if (noneAreSymbolic) {
-        let output = this.call(inputs as Tensor | Tensor[], kwargs);
+        let output = this.call(inputs, kwargs);
 
         // Apply masks to the output tensors if the layer supports it.
         if (this.supportsMasking) {
           // TODO(mattsoulanille): pass the input tensors' masks to computeMask
-          const outputMask = this.computeMask(inputs as Tensor | Tensor[]);
-          if (output instanceof Array && outputMask instanceof Array) {
-            if (output.length !== outputMask.length) {
-              throw new Error(`${this.name} output ${output.length} tensors `
-                + `but ${outputMask.length} masks for those tensors`);
-            }
-            for (let i = 0; i < output.length; i++) {
-              output[i].kerasMask = outputMask[i];
-            }
-          } else if (outputMask instanceof Array) {
-            throw new Error(`{this.name} output a single tensor `
-              + `but ${outputMask.length} masks`);
-          } else if (output instanceof Array) {
-            for (const out of output) {
-              out.kerasMask = outputMask.clone();
-            }
-            outputMask.dispose(); // Only keep the clones to avoid leaking
-          } else {
-            output.kerasMask = outputMask;
-          }
+          this.setMaskMetadata(inputs, output);
         }
 
         // If the layer returns tensors from its inputs, unmodified,
@@ -1097,8 +1066,7 @@ export abstract class Layer extends serialization.Serializable {
           If the input tensor(s) had no previous history,
           this does nothing.
         */
-        this.addInboundNode(
-            inputs as SymbolicTensor | SymbolicTensor[], output, null, null,
+        this.addInboundNode(inputs, output, null, null,
             inputShape, outputShape, kwargs);
         this._refCount++;
 
@@ -1419,6 +1387,32 @@ export abstract class Layer extends serialization.Serializable {
     return mask;
   }
 
+  private setMaskMetadata(inputs: Tensor|Tensor[], outputs: Tensor|Tensor[],
+                          previousMask?: Tensor|Tensor[]): void {
+    if (!this.supportsMasking) {
+      return;
+    }
+
+    const outputMasks = this.computeMask(inputs, previousMask);
+    if (outputs instanceof Array && outputMasks instanceof Array) {
+      if (outputs.length !== outputMasks.length) {
+        throw new Error(`${this.name} outputs ${outputs.length} tensors `
+          + `but ${outputMasks.length} masks for those tensors`);
+      }
+      for (let i = 0; i < outputs.length; i++) {
+        outputs[i].kerasMask = outputMasks[i];
+      }
+    } else if (outputMasks instanceof Array) {
+      throw new Error(`{this.name} outputs a single tensor `
+        + `but ${outputMasks.length} masks`);
+    } else if (outputs instanceof Array) {
+      throw new Error(`{this.name} outputs ${outputs.length} tensors `
+        + `but only one mask`);
+    } else {
+      outputs.kerasMask = outputMasks;
+    }
+  }
+
   /**
    * Internal method to create an inbound node for the layer.
    *
@@ -1666,3 +1660,29 @@ export function getSourceInputs(
     }
   }
 }
+
+type MaybeSymbolic = SymbolicTensor | Tensor;
+
+function checkAllSymbolic(tensors: MaybeSymbolic | MaybeSymbolic[]
+                         ): tensors is SymbolicTensor | SymbolicTensor[] {
+  let allAreSymbolic = true;
+  for (const tensor of generic_utils.toList(tensors)) {
+    if (!(tensor instanceof SymbolicTensor)) {
+      allAreSymbolic = false;
+      break;
+    }
+  }
+  return allAreSymbolic;
+}
+
+function checkNoneSymbolic(tensors: MaybeSymbolic | MaybeSymbolic[]
+                          ): tensors is Tensor | Tensor[] {
+  let noneAreSymbolic = true;
+  for (const tensor of generic_utils.toList(tensors)) {
+    if (tensor instanceof SymbolicTensor) {
+      noneAreSymbolic = false;
+      break;
+    }
+  }
+  return noneAreSymbolic;
+}
diff --git a/tfjs-layers/src/utils/generic_utils.ts b/tfjs-layers/src/utils/generic_utils.ts
index 2d06342c8e1..c128097f5ed 100644
--- a/tfjs-layers/src/utils/generic_utils.ts
+++ b/tfjs-layers/src/utils/generic_utils.ts
@@ -76,7 +76,7 @@ export function singletonOrArray<T>(xs: T[]): T|T[] {
  * @param x target object to be normalized.
  */
 // tslint:disable-next-line:no-any
-export function toList(x: any): any[] {
+export function toList<T>(x: T|T[]): T[] {
   if (Array.isArray(x)) {
     return x;
   }