const std::vector<ArgView> &Inputs() const;
  const std::vector<ArgView> &Outputs() const;
  const std::vector<AttrView> &Attributes() const;
  const std::vector<OpArgumentView> &AllArguments() const;

  int NumInputs() const;
  int NumOutputs() const;
  ArgView OnlyInput() const;
  ArgView OnlyOutput() const;

  string FunctionName() const;
  string VariableName() const;
  string OpNameString() const;

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();
        }
      };

  const auto &inputs_ty = op.getInputs().getType();
  int n_inputs = inputs_ty.size();
  if (n_inputs < 1) return op.emitOpError() << "No inputs";

  const auto &init_values_ty = op.getInitValues().getType();
  int n_init_values = init_values_ty.size();
  if (n_init_values != n_inputs) {
    return op.emitOpError() << "Number of inputs (" << n_inputs
                            << ") is different than number of init_values ("

def LowerSoftmaxCrossEntropyWithLogitsOp : Pattern<
  (TF_SoftmaxCrossEntropyWithLogitsOp AnyRankedTensor:$features,
                                      AnyRankedTensor:$labels),
  [(TF_SumOp (TF_MulNoNanOp:$sum_input
                     (TF_LogSoftmaxOp $features),
                     (TF_NegOp $labels)),
             (TF_ConstOp (GetI64ScalarElementsAttr<-1>)),
             /*keep_dims=*/ConstBoolAttrFalse),

		posRow:    -1,
		rows:      setMRows,
		cols:      setColumns,
		colType:   setColType,
		errPos:    -1,
	}
	return cursor, nil
}

func (s *fakeStmt) NumInput() int {
	if s.panic == "NumInput" {
		panic(s.panic)
	}
	return s.placeholders
}

// hook to simulate broken connections
var hookCommitBadConn func() bool

func (tx *fakeTx) Commit() error {