: OpKernel(ctx), platform_info_(XlaPlatformInfoFromDevice(ctx->device())) {}

void XlaRunOp::Compute(OpKernelContext* ctx) {
  VLOG(3) << "XlaRunOp " << def().name();
  Tensor key_tensor = ctx->input(ctx->num_inputs() - 1);

  bool use_pjrt =
      GetXlaOpsCommonFlags()
          ->tf_xla_use_device_api.IsEnabledInXlaCompileAndRunForDevice(
              platform_info_.device_type());

  if (use_pjrt) {

    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.

      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),