text: /* Copyright 2017 The TensorFlow Authors. 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.
  ==============================================================================*/
  #include "tensorflow/core/framework/dataset.h"
  #include "tensorflow/core/framework/partial_tensor_shape.h"
  #include "tensorflow/core/framework/tensor.h"
  
  namespace tensorflow {
  namespace data {
  namespace {
  
  // See documentation in ../ops/dataset_ops.cc for a high-level
  // description of the following op.
  
  class IgnoreErrorsDatasetOp : public UnaryDatasetOpKernel {
   public:
    explicit IgnoreErrorsDatasetOp(OpKernelConstruction* ctx)
        : UnaryDatasetOpKernel(ctx) {}
  
    void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
                     DatasetBase** output) override {
      *output = new Dataset(ctx, input);
    }
  
   private:
    class Dataset : public DatasetBase {
     public:
      explicit Dataset(OpKernelContext* ctx, const DatasetBase* input)
          : DatasetBase(DatasetContext(ctx)), input_(input) {
        input_->Ref();
      }
  
      ~Dataset() override { input_->Unref(); }
  
      std::unique_ptr<IteratorBase> MakeIteratorInternal(
          const string& prefix) const override {
        return std::unique_ptr<IteratorBase>(
            new Iterator({this, strings::StrCat(prefix, "::IgnoreErrors")}));
      }
  
      const DataTypeVector& output_dtypes() const override {
        return input_->output_dtypes();
      }
      const std::vector<PartialTensorShape>& output_shapes() const override {
        return input_->output_shapes();
      }
  
      string DebugString() const override {
        return "IgnoreErrorsDatasetOp::Dataset";
      }
  
      int64 Cardinality() const override { return input_->Cardinality(); }
  
     protected:
      Status AsGraphDefInternal(SerializationContext* ctx,
                                DatasetGraphDefBuilder* b,
                                Node** output) const override {
        Node* input_graph_node = nullptr;
        TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, input_, &input_graph_node));
        TF_RETURN_IF_ERROR(b->AddDataset(this, {input_graph_node}, output));
        return Status::OK();
      }
  
     private:
      class Iterator : public DatasetIterator<Dataset> {
       public:
        explicit Iterator(const Params& params)
            : DatasetIterator<Dataset>(params) {}
  
        Status Initialize(IteratorContext* ctx) override {
          return dataset()->input_->MakeIterator(ctx, prefix(), &input_impl_);
        }
  
        Status GetNextInternal(IteratorContext* ctx,
                               std::vector<Tensor>* out_tensors,
                               bool* end_of_sequence) override {
          {
            tf_shared_lock l(mu_);
            if (!input_impl_) {
              *end_of_sequence = true;
              return Status::OK();
            }
            Status s = input_impl_->GetNext(ctx, out_tensors, end_of_sequence);
            while (!s.ok()) {
              out_tensors->clear();
              s = input_impl_->GetNext(ctx, out_tensors, end_of_sequence);
            }
          }
          if (*end_of_sequence) {
            mutex_lock l(mu_);
            input_impl_.reset();
          }
          return Status::OK();
        }
  
       protected:
        std::shared_ptr<model::Node> CreateNode(
            IteratorContext* ctx, model::Node::Args args) const override {
          return model::MakeKnownRatioNode(std::move(args),
                                           /*ratio=*/1);
        }
  
        Status SaveInternal(IteratorStateWriter* writer) override {
          mutex_lock l(mu_);
          if (input_impl_)
            TF_RETURN_IF_ERROR(SaveInput(writer, input_impl_));
          else
            TF_RETURN_IF_ERROR(
                writer->WriteScalar(full_name("input_impls_empty"), ""));
          return Status::OK();
        }
  
        Status RestoreInternal(IteratorContext* ctx,
                               IteratorStateReader* reader) override {
          mutex_lock l(mu_);
          if (reader->Contains(full_name("input_impls_empty")))
            input_impl_.reset();
          else
            TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_));
          return Status::OK();
        }
  
       private:
        mutex mu_;
        std::unique_ptr<IteratorBase> input_impl_ GUARDED_BY(mu_);
      };
  
      const DatasetBase* const input_;
    };
  };
  
  REGISTER_KERNEL_BUILDER(
      Name("ExperimentalIgnoreErrorsDataset").Device(DEVICE_CPU),
      IgnoreErrorsDatasetOp);
  
  }  // namespace
  }  // namespace data
  }  // namespace tensorflow
  