{call: "Setresgid(0,102,101)", fn: func() error { return syscall.Setresgid(0, 102, 101) }, filter: "Gid:", expect: "\t0\t102\t101\t102"},
		{call: "Setresgid(0,0,0)", fn: func() error { return syscall.Setresgid(0, 0, 0) }, filter: "Gid:", expect: "\t0\t0\t0\t0"},

		{call: "Setresuid(1,0,2)", fn: func() error { return syscall.Setresuid(1, 0, 2) }, filter: "Uid:", expect: "\t1\t0\t2\t0"},

    // Wire input/output nodes.
    new_dequantize_op->setOperand(0, user_op->getResult(0));
    new_dequantize_op->getResult(0).setType(user_op->getResult(0).getType());
    user_op->getResult(0).replaceAllUsesExcept(new_dequantize_op->getResult(0),
                                               new_dequantize_op);
    user_op->getResult(0).setType(new_user_op_type);
  }

  LogicalResult matchAndRewrite(TF::PartitionedCallOp op,

  for (auto *var_handle_user : var_handle_op.getResult().getUsers()) {
    auto read_variable_op = dyn_cast_or_null<ReadVariableOp>(var_handle_user);
    if (!read_variable_op) continue;
    for (auto *read_variable_user : read_variable_op.getResult().getUsers()) {
      auto q_op = dyn_cast_or_null<QuantizeOp>(read_variable_user);
      if (!q_op || ref_qtype) continue;
      ref_qtype = q_op.getResult().getType();
    }
  }
  return ref_qtype;

                .build()
                .run()
        }

        then:
        projectsLoadedHandler.getResult() == "loading"
        buildFinishedHandler.getResult() == "build"
    }

    def "failures from action are received and future actions not run"() {
        def projectsLoadedHandler = new IntermediateResultHandlerCollector()

    rewriter.replaceAllUsesWith(filter_op->getResult(0),
                                quantized_filter_constant_op.getResult());

    // Replace conv op with a new convolution op that has quantized output type.
    // Quantize -> Dequantize following r3.
    auto output_uniform_quantize_call_op = cast<func::CallOp>(
        *combined_scale_multiply_op.getResult().user_begin());

           : recurrent_weights_array->getResult(1);
  Value recurrent_to_cell_weights =
      indy ? mlir::cast<Value>(
                 CreateFlattenOP(recurrent_weights_array->getResult(2),
                                 func_op.getLoc(), builder)
                     .getResult())
           : recurrent_weights_array->getResult(2);
  Value recurrent_to_output_weights =
      indy ? mlir::cast<Value>(

    // Compute inverse of input transpose.
    llvm::SmallVector<int32_t> inverse_perm = InvertPermutation(perm_arr);

    if (tpose_arg.getResult().getType() != op->getResult(0).getType()) {
      // If types are not equal here than the transpose is taking place
      // before an implicit broadcast in the elementwise. We need

    b.setInsertionPoint(op);
    if (auto dq = llvm::dyn_cast<quantfork::DequantizeCastOp>(op)) {
      auto dcast = b.create<DequantizeOp>(dq.getLoc(), dq.getResult().getType(),
                                          dq.getArg());
      dq.getResult().replaceAllUsesWith(dcast);
      if (auto extra_attr = op->getAttr(mlir::quant::kVolatileOpAttrName)) {
        dcast->setAttr(mlir::quant::kVolatileOpAttrName, extra_attr);
      }

  SmallVector<Value, 4> while_input_values;
  SmallVector<Type, 4> while_input_types;
  while_input_values.push_back(const_true.getResult());
  while_input_types.push_back(const_true.getResult().getType());
  for (int i = 1; i < reduce_dataset.getNumOperands(); ++i) {
    while_input_values.push_back(reduce_dataset.getOperand(i));

    op.setOperand(0, stb_op.getInput());
    op.getResult().setType(bts_op.getResult().getType());
  }

  if (final_op_is_bts) {
    if (bts_op.getInput().getDefiningOp<TF::PadOp>()) {
      bts_op.getResult().replaceAllUsesWith(pad_op.getInput());
    } else {
      bts_op.getResult().replaceAllUsesWith(bts_op.getInput());
    }
  }

  stb_op.getResult().dropAllUses();
  return success();
}