TFE_CancellationManager*, TFE_CancellationToken token);
TF_CAPI_EXPORT extern void TFE_DeleteCancellationManager(
    TFE_CancellationManager*);

// Associates the given `cancellation_manager` with `op`, so that invoking
// `TFE_CancellationManagerStartCancel(cancellation_manager)` will cancel the
// execution of `op`.
typedef struct TFE_CancellationManager TFE_CancellationManager;
TF_CAPI_EXPORT extern void TFE_OpSetCancellationManager(

  ASSERT_EQ(1, TF_OperationNumInputs(neg));
  TF_Output neg_input = TF_OperationInput({neg, 0});
  EXPECT_EQ(scalar, neg_input.oper);
  EXPECT_EQ(0, neg_input.index);

  // Test that we can't see control edges involving the source and sink nodes.
  TF_Operation* control_ops[100];
  EXPECT_EQ(0, TF_OperationNumControlInputs(scalar));
  EXPECT_EQ(0, TF_OperationGetControlInputs(scalar, control_ops, 100));

### Typical Pull Request Workflow -

**1. New PR**

- As a contributor, you submit a New PR on GitHub.
- We inspect every incoming PR and add certain labels to the PR such as `size:`,
  `comp:` etc.  At this stage we check if the PR is valid and meets certain
  quality requirements. For example, we check if the CLA is signed, PR has

 private:
  AbstractTensorHandle* handle_;
};

// A tracing/immediate-execution agnostic tape.
//
// Gradient functions defined for this tape must support handling null incoming
// gradients.
class Tape : protected eager::GradientTape<AbstractTensorHandle,
                                           GradientFunction, TapeTensor> {
 public:

*   `tf.debugging`
    *   Add `tf.debugging.enable_check_numerics()` and
        `tf.debugging.disable_check_numerics()` to help debugging the root
        causes of issues involving infinities and `NaN`s.
*   `tf.distribute`
    *   Custom training loop support on TPUs and TPU pods is available through
        `strategy.experimental_distribute_dataset`,

  // `output_tensors` on the tape and marks all its outputs as watched if at
  // least one input of the op is watched and has trainable dtype.
  //
  // op_type is used to decide which of the incoming gradients can be left as
  // nullptr instead of building zeros when build_default_zeros_grads == true.
  void RecordOperation(
      const string& op_type, const std::vector<TapeTensor>& output_tensors,