Add kernel RaggedTensorToTensor for CPU and WebGL backend (#6686)

* Add kernel RaggedTensorToTensor for CPU and WebGL backend

* Add WebGL kernel forward

* Change version

Author: ahmedsabie

tfjs-backend-cpu/src/kernels/RaggedTensorToTensor.ts

@@ -0,0 +1,52 @@
+/**
+ * @license
+ * Copyright 2022 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+import {KernelConfig, KernelFunc, RaggedTensorToTensor, RaggedTensorToTensorAttrs, RaggedTensorToTensorInputs, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';
+
+import {MathBackendCPU} from '../backend_cpu';
+
+import {raggedTensorToTensorImpl} from './RaggedTensorToTensor_impl';
+
+export function raggedTensorToTensor(args: {
+  inputs: RaggedTensorToTensorInputs,
+  backend: MathBackendCPU,
+  attrs: RaggedTensorToTensorAttrs
+}): TensorInfo {
+  const {inputs, backend, attrs} = args;
+  const {shape, values, defaultValue, rowPartitionTensors} = inputs;
+  const {rowPartitionTypes} = attrs;
+
+  const $shape = backend.data.get(shape.dataId).values as TypedArray;
+  const $values = backend.data.get(values.dataId).values as TypedArray;
+  const $defaultValue =
+      backend.data.get(defaultValue.dataId).values as TypedArray;
+  const $rowPartitionValues = rowPartitionTensors.map(
+      t => backend.data.get(t.dataId).values as TypedArray);
+  const rowPartitionValuesShapes = rowPartitionTensors.map(t => t.shape);
+
+  const [outputShape, output] = raggedTensorToTensorImpl(
+      $shape, shape.shape, $values, values.shape, values.dtype, $defaultValue,
+      defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes,
+      rowPartitionTypes);
+  return backend.makeTensorInfo(outputShape, values.dtype, output);
+}
+
+export const raggedTensorToTensorConfig: KernelConfig = {
+  kernelName: RaggedTensorToTensor,
+  backendName: 'cpu',
+  kernelFunc: raggedTensorToTensor as {} as KernelFunc,
+};