// 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,
      absl::Span<const int64_t> input_tensor_id,

    }
    if (raw_device_name) {
      device_name_ = raw_device_name;
    }
    op_type_ = op;
    return absl::OkStatus();
  }
  Status SetOpName(const char* const op_name) override {
    if (op_) {
      return errors::FailedPrecondition(
          "SetOpName called on already built op.");
    }
    if (op_type_.empty()) {
      return errors::FailedPrecondition(

      gtl::ArraySlice<AbstractTensorHandle*> gradient_tensors) const override;

  // Calls the passed-in backward function.
  // op_type is the op's name provided in RecordOperation.
  Status CallBackwardFunction(
      const string& op_type, GradientFunction* gradient_function,
      const std::vector<int64_t>& unneeded_gradients,
      gtl::ArraySlice<AbstractTensorHandle*> output_gradients,

  return input_arg.number_attr().c_str();
}

int TF_OpIsStateful(const char* op_type, TF_Status* status) {
  const tensorflow::OpRegistrationData* op_reg_data;
  status->status =
      tensorflow::OpRegistry::Global()->LookUp(op_type, &op_reg_data);
  if (!status->status.ok()) {
    return 0;
  }
  return op_reg_data->op_def.is_stateful();
}

    const char* op_name, int input_index, TF_Status* status);

// Returns 1 if the op is stateful, 0 otherwise. The return value is undefined
// if the status is not ok.
TF_CAPI_EXPORT extern int TF_OpIsStateful(const char* op_type,
                                          TF_Status* status);

// Platform specific initialization routine. Very few platforms actually require
// this to be called.

		{ObjectOpts{Name: "c1test", VersionID: "vid", DeleteMarker: true, SSEC: true, OpType: DeleteReplicationType}, cfgs[0], true}, // 7. permanent delete of version

    ParameterizedType ourType =
        Types.newParameterizedType(HashMap.class, String.class, int[][].class);

    new EqualsTester().addEqualityGroup(jvmType, ourType).testEquals();
    assertEquals(jvmType.toString(), ourType.toString());
    assertEquals(jvmType.hashCode(), ourType.hashCode());
    assertEquals(HashMap.class, ourType.getRawType());
    assertThat(ourType.getActualTypeArguments())
        .asList()

	case replication.Pending:
		if ri.OpType.IsDataReplication() && prevStatus != status {
			rs.set(ri.Arn, ri.Size, 0, status, ri.OpType, ri.endpoint, ri.secure, ri.Err)
		}
	case replication.Completed:
		if ri.OpType.IsDataReplication() {
			rs.set(ri.Arn, ri.Size, ri.Duration, status, ri.OpType, ri.endpoint, ri.secure, ri.Err)
		}
	case replication.Failed:
		if ri.OpType.IsDataReplication() && prevStatus == replication.Pending {

    ParameterizedType ourType =
        Types.newParameterizedType(HashMap.class, String.class, int[][].class);

    new EqualsTester().addEqualityGroup(jvmType, ourType).testEquals();
    assertEquals(jvmType.toString(), ourType.toString());
    assertEquals(jvmType.hashCode(), ourType.hashCode());
    assertEquals(HashMap.class, ourType.getRawType());
    assertThat(ourType.getActualTypeArguments())
        .asList()

        upEnd = other.getUpperEndpoint();
        upType = other.getUpperBoundType();
      }
    }

    if (hasLowBound && hasUpBound) {
      int cmp = comparator.compare(lowEnd, upEnd);
      if (cmp > 0 || (cmp == 0 && lowType == OPEN && upType == OPEN)) {
        // force allowed empty range
        lowEnd = upEnd;
        lowType = OPEN;
        upType = CLOSED;
      }
    }