return f.abs
}

func (f *outputdirFlag) Set(value string) (err error) {
	if value == "" {
		f.abs = ""
	} else {
		f.abs, err = filepath.Abs(value)
	}
	return err
}

func (f *outputdirFlag) getAbs() string {
	if f.abs == "" {
		return base.Cwd()
	}
	return f.abs
}

// vetFlag implements the special parsing logic for the -vet flag:
// a comma-separated list, with distinguished values "all" and

                                PatternRewriter& rewriter) const override {
    auto preceding_qcast =
        q_op.getArg().getDefiningOp<quantfork::QuantizeCastOp>();
    if (!preceding_qcast) return failure();

    auto new_qcast = rewriter.create<quantfork::QuantizeCastOp>(
        q_op.getLoc(), q_op.getType(), preceding_qcast.getArg());
    new_qcast->setAttr(kVolatileOpAttrName, rewriter.getUnitAttr());
    q_op->replaceAllUsesWith(new_qcast);

                contextualLabel == 'Type \'MyTask\' property \'badTime\' is private and annotated with @Input'
                details == 'Annotations on private getters are ignored'
                solutions == [
                    'Make the getter public',
                    'Annotate the public version of the getter',
                ]
                additionalData.asMap == [

  // Finds the mapping from a replicate argument to an execute operand.
  llvm::SmallDenseMap<int64_t, int64_t, 8> replicate_arg_to_execute_arg;
  for (auto index_and_arg : llvm::enumerate(execute.getArgs())) {
    auto arg = SkipIdentity(index_and_arg.value(), /*allow_other_use=*/false);
    if (!arg.hasOneUse() ||
        !mlir::isa<TF::ResourceType>(getElementTypeOrSelf(arg.getType()))) {
      continue;
    }

            statement.setString(2, displayInfo.getDisplayName());
            statement.setObject(3, toArray(displayInfo.getTasksToRun()));
            statement.setObject(4, toArray(displayInfo.getArgs()));
            statement.setObject(5, toArray(displayInfo.getGradleOpts()));
            statement.setBoolean(6, displayInfo.getDaemon());

              .getOutputDimensions()
              .getDefiningOp<shape::ShapeOfOp>();
      // Check if the shape come from the original conv op.
      if (!shape_of_op ||
          shape_of_op.getArg().getDefiningOp<mhlo::ConvolutionOp>() !=
              conv_op) {
        return failure();
      }
      Value new_shape_of = rewriter.create<shape::ShapeOfOp>(

from starlette.requests import HTTPConnection, Request
from starlette.responses import Response
from starlette.websockets import WebSocket
from typing_extensions import Annotated, get_args, get_origin

multipart_not_installed_error = (
    'Form data requires "python-multipart" to be installed. \n'
    'You can install "python-multipart" with: \n\n'
    "pip install python-multipart\n"
)

      tensorflow::XlaCallModuleLoader::Create(
          context, static_cast<int>(op.getVersion()), op.getModule().str(),
          std::move(disabled_checks), std::move(platforms),
          /*num_invocation_args=*/op.getArgs().size(),
          op.getHasTokenInputOutput()));
  return std::move(*loader).module();
}

// Renames functions in the stablehlo module to avoid naming conflicts with
// existing functions in the tf module.

            experimentSpec.getInvocation().getBuildAction(),
            cleanTasks.isEmpty()
                ? BuildAction.NO_OP
                : new RunTasksAction(cleanTasks),
            invocationSpec.getArgs(),
            invocationSettings.getSystemProperties(),
            collectMutators(invocationSettings, experimentSpec),
            invocationSettings.getWarmUpCount(),
            invocationSettings.getBuildCount(),

    ModuleOp module = op->getParentOfType<ModuleOp>();
    SymbolTable symbol_table(module);
    func::FuncOp float_func =
        dyn_cast<func::FuncOp>(symbol_table.lookup(function_name));
    OperandRange func_args = op.getArgs();
    func::FuncOp new_float_func = float_func.clone();

    SmallVector<Value> new_float_func_args{func_args.begin(), func_args.end()};
    new_float_func_args[weight_operand_idx] = reshape_op;