TFE_DeleteTensorHandle(input2);
  TFE_DeleteTensorHandle(identity_ret[0]);
  TFE_DeleteTensorHandle(identity_ret[1]);

  TFE_DeleteOp(cloned_identity);
  TFE_DeleteOp(original_identity);
  TFE_DeleteOp(original_var_op);
  TFE_DeleteOp(cloned);
  TF_DeleteStatus(status);
  TFE_DeleteContext(ctx);
}

TEST(CAPI, ShareVariableAcrossContextsWorks) {

    std::vector<TFE_TensorHandle*> result;
    result.push_back(nullptr);
    int num_retvals = 1;
    TFE_Execute(op, result.data(), &num_retvals, status);
    TFE_DeleteOp(op);
    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    ASSERT_EQ(num_retvals, 1);

    TF_Tensor* r = TFE_TensorHandleResolve(result[0], status);

typedef struct TFE_TensorDebugInfo TFE_TensorDebugInfo;

// Retrieves TFE_TensorDebugInfo for `handle`.
// If TFE_TensorHandleTensorDebugInfo succeeds, `status` is set to OK and caller
// is responsible for deleting returned TFE_TensorDebugInfo.
// If TFE_TensorHandleTensorDebugInfo fails, `status` is set to appropriate
// error and nullptr is returned. This function can block till the operation
// that produces `handle` has completed.

                    /*input_shapes*/ {unknown_shape(), unknown_shape()},
                    /*input_tensors*/ {},
                    /*expected_shape*/ make_shape({kUnknownDim, kUnknownDim}));

  TFE_DeleteOp(matmul_op);
  // TODO(bgogul): Add some death tests where status is not OK.
}

TEST_F(ShapeInferenceTest, InfersShapesFromInputTensors) {
  // Prepare some tensors for shape.

  TFE_TensorHandle* var_handle = nullptr;
  int num_retvals = 1;
  TFE_Execute(op, &var_handle, &num_retvals, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_DeleteOp(op);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  CHECK_EQ(1, num_retvals);

  // Assign 'value' to it.
  op = TFE_NewOp(ctx, "AssignVariableOp", status);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  // a trainable dtype.
  bool ShouldRecord(
      absl::Span<const AbstractTensorHandle* const> tensors) const;
  // Unwatches this tensor on the tape. Mainly used for cleanup when deleting
  // eager tensors.
  void DeleteTrace(const AbstractTensorHandle*);

  // Consumes the internal state of the tape (so cannot be called more than

  status->status = new_op->Reset(op_or_function_name, nullptr);
  if (!status->status.ok()) {
    new_op->Release();
    new_op = nullptr;
  }
  return tensorflow::wrap(new_op);
}

void TFE_DeleteOp(TFE_Op* op) {
  if (op == nullptr) {
    return;
  }

  tensorflow::unwrap(op)->Release();
}

const char* TFE_OpGetName(const TFE_Op* op, TF_Status* status) {

    `tf.train.experimental.enable_mixed_precision_graph_rewrite()`.
*   Add environment variable `TF_CUDNN_DETERMINISTIC`. Setting to "true" or "1"
    forces the selection of deterministic cuDNN convolution and max-pooling
    algorithms. When this is enabled, the algorithm selection procedure itself
    is also deterministic.

## Breaking Changes

*   Many backwards incompatible API changes have been made to clean up the APIs

// * size_t is used to represent byte sizes of objects that are
//   materialized in the address space of the calling process.
// * int is used as an index into arrays.
// * Deletion functions are safe to call on nullptr.
//
// Questions left to address:
// * Might at some point need a way for callers to provide their own Env.
// * Maybe add TF_TensorShape that encapsulates dimension info.
//

  CheckTFE_TensorHandleHasFloats(retval_task0, {7, 10, 15, 22});

  TFE_DeleteTensorHandle(retval_task0);
  TFE_DeleteTensorHandle(h0_task0);
  TFE_DeleteTensorHandle(retvals[0]);

  TFE_DeleteOp(matmul);

  TFE_Executor* executor = TFE_ContextGetExecutorForThread(ctx);
  TFE_ExecutorWaitForAllPendingNodes(executor, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteExecutor(executor);