CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddAttrs(ret, TFE_OpGetAttrs(other));
  int num_inputs = TFE_OpGetFlatInputCount(other, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  for (int input_index = 0; input_index < num_inputs; ++input_index) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(other, input_index, status);

// For inputs that take a list of tensors.
// inputs must point to TF_Output[num_inputs].
TF_CAPI_EXPORT extern void TF_AddInputList(TF_OperationDescription* desc,
                                           const TF_Output* inputs,
                                           int num_inputs);

// Call once per control input to `desc`.

    mlir::FunctionType callee_type = callee.getFunctionType();
    SmallVector<Type, 4> expected_callee_input_types;
    auto num_inputs = op.getInputs().size() / 2;
    for (unsigned i = 0; i < num_inputs; ++i) {
      auto input_type =
          mlir::dyn_cast<RankedTensorType>(op.getOperand(i).getType());
      auto scalar = RankedTensorType::get({}, input_type.getElementType());

                                        ? mhlo::Precision::DEFAULT
                                        : mlir::mhlo::Precision::HIGHEST;
  llvm::SmallVector<mlir::Attribute, 2> attr_vec;
  const int num_inputs = 2;
  for (int i = 0; i < num_inputs; i++) {
    attr_vec.push_back(
        mlir::mhlo::PrecisionAttr::get(builder->getContext(), precision));
  }
  return builder->getArrayAttr(attr_vec);
}

const std::vector<AttrView>& OpView::Attributes() const {
  return argument_attrs_;
}

const std::vector<OpArgumentView>& OpView::AllArguments() const {
  return all_arguments_;
}

int OpView::NumInputs() const { return input_args_.size(); }

int OpView::NumOutputs() const { return output_args_.size(); }

ArgView OpView::OnlyInput() const {
  CHECK_EQ(input_args_.size(), 1);  // Crash OK
  return input_args_.front();

      }
    }

    return mlir::UnrankedTensorType::get(element_type);
  };

  // Below we only try and do some shape inference for "source" ops which have
  // no inputs.
  if (node.num_inputs() > 0) return default_type();

  // Do some simply inference here to get the function arguments correct for
  // this common case.
  // TODO(jpienaar): Reconsider post refactoring shape functions.

	// do not block indefinitely (e.g. apply a timeout).
	Close() error

	// NumInput returns the number of placeholder parameters.
	//
	// If NumInput returns >= 0, the sql package will sanity check
	// argument counts from callers and return errors to the caller
	// before the statement's Exec or Query methods are called.
	//
	// NumInput may also return -1, if the driver doesn't know

      IsSingleCallRegion(while_region.getBody()), while_arg_matcher);

  // All existing inputs to while region are inputs to the functional while.
  auto new_inputs = llvm::to_vector<4>(while_region.getOperands());

  // All existing results will also be generated by the functional while.
  auto new_result_types = llvm::to_vector<4>(while_region.getResultTypes());

					return err
				}
				covervarsoutfile = outputfiles[0]
				outputfiles = outputfiles[1:]
				numInputs := len(flag.Args())
				numOutputs := len(outputfiles)
				if numOutputs != numInputs {
					return fmt.Errorf("number of output files (%d) not equal to number of input files (%d)", numOutputs, numInputs)
				}
				if err := readPackageConfig(*pkgcfg); err != nil {
					return err
				}
				return nil

/**
 * Tests for SimpleGenericBloomFilter and derived BloomFilter views.
 *
 * @author Dimitris Andreou
 */
public class BloomFilterTest extends TestCase {
  private static final int NUM_PUTS = 100_000;
  private static final ThreadLocal<Random> random =
      new ThreadLocal<Random>() {
        @Override
        protected Random initialValue() {
          return new Random();
        }
      };