Change for internal compatibility.

tensorflow/core/grappler/clusters/single_machine.cc

@@ -41,7 +41,7 @@ SingleMachine::SingleMachine(int timeout_s, int num_cpu_cores, int num_gpus)
 }
 
 SingleMachine::~SingleMachine() {
-  CloseSession(false /*use_timeout*/);
+  CloseSession(false /*use_timeout*/).IgnoreError();
 
   // Prevent the destructor from deleting mu_ until CloseSession() is done.
   mutex_lock l(mu_);
@@ -164,18 +164,18 @@ Status SingleMachine::CloseSession(bool use_timeout) {
 
     thread_pool_->Schedule([this] {
       if (this->coordinator_) {
-        this->coordinator_->RequestStop();
+        this->coordinator_->RequestStop().IgnoreError();
         // Wait for all the runners to have closed their queues.
         while (!this->coordinator_->AllRunnersStopped()) {
           sleep(1);
         }
         // Now we can close the session. This should cancel any pending I/O
         // operation.
-        this->session_->Close();
+        this->session_->Close().IgnoreError();
         // Last but not least, we can delete the coordinator.
         this->coordinator_.reset();
       } else {
-        this->session_->Close();
+        this->session_->Close().IgnoreError();
       }
 
       // Wait for any previous run to finish.

tensorflow/core/kernels/fused_batch_norm_op.cc

@@ -300,8 +300,9 @@ struct FusedBatchNorm<GPUDevice, T> {
     GPUDevice d = context->eigen_device<GPUDevice>();
     using perftools::gputools::DeviceMemory;
     Tensor inv_var;
-    context->allocate_temp(DataTypeToEnum<T>::value, estimated_variance.shape(),
-                           &inv_var);
+    OP_REQUIRES_OK(
+        context, context->allocate_temp(DataTypeToEnum<T>::value,
+                                        estimated_variance.shape(), &inv_var));
     auto inv_var_ptr = StreamExecutorUtil::AsDeviceMemory<T>(inv_var);
     std::function<const DeviceMemory<T>&()> var_to_inv_var =
         [d, epsilon, estimated_variance,

tensorflow/core/kernels/hexagon/graph_transferer.cc

@@ -96,7 +96,8 @@ Status GraphTransferer::LoadGraphFromProto(
           shape_inference::ShapeHandle handle;
           status = context->MakeShapeFromTensorShape(
               input_node_info.second.shape(), &handle);
-          shape_refiner.SetShape(node, 0, handle);
+          // TODO(b/32704451): Don't just ignore this status!
+          shape_refiner.SetShape(node, 0, handle).IgnoreError();
           is_input_node = true;
         }
         if (!status.ok()) {
@@ -395,9 +396,11 @@ void GraphTransferer::RegisterConstantNode(
   const_node_info.add_shape(shape[2]);
   const_node_info.add_shape(shape[3]);
   const TensorProto* proto = nullptr;
-  GetNodeAttr(node.def(), "value", &proto);
+  // TODO(b/32704451): Don't just ignore this status!
+  GetNodeAttr(node.def(), "value", &proto).IgnoreError();
   Tensor const_tensor;
-  MakeTensorFromProto(*proto, &const_tensor);
+  // TODO(b/32704451): Don't just ignore this status!
+  MakeTensorFromProto(*proto, &const_tensor).IgnoreError();
 
   const_node_info.set_dtype(const_tensor.dtype());
   // TODO(satok): Remove. Determine constant value without dryrun

tensorflow/tools/graph_transforms/sparsify_gather.cc

@@ -146,7 +146,7 @@ Status SparsifyGather(const GraphDef& input_graph_def,
           const NodeDef& const_node = match.inputs[0].inputs[0].node;
 
           DataType data_type;
-          GetNodeAttr(const_node, "dtype", &data_type);
+          TF_RETURN_IF_ERROR(GetNodeAttr(const_node, "dtype", &data_type));
           if (data_type != DT_FLOAT) {
             return tensorflow::errors::FailedPrecondition(
                 "Transform only applicable to subgraph with 'Const' of dtype "