llvm::SmallVector<mlir::Type, 4> output_tys;
    for (int i = 0; i < custom_op.getNumResults(); i++) {
      output_tys.push_back(custom_op.getType(i));
    }
    op_state.addTypes(output_tys);
    op_state.addAttributes(attr);
    auto stablehlo_op = builder.create(op_state);
    custom_op.replaceAllUsesWith(stablehlo_op);
    custom_op.erase();
  });
}

                      << ". Please make sure the op library for "
                      << parsed_op_name << " is linked properly";
      return failure();
    }
    op_state.addAttributes(attrs);

    Operation* tf_op = rewriter.create(op_state);
    rewriter.replaceOp(op, tf_op->getResults());

    // Special type fixes for TF Resource Tensors that are casted to

    private MyToken prevToken;

    private final List<MyToken> synonyms;

    private final CharTermAttribute termAttr = addAttribute(CharTermAttribute.class);

    private final OffsetAttribute offsetAttr = addAttribute(OffsetAttribute.class);

    private final PositionIncrementAttribute posIncAttr = addAttribute(PositionIncrementAttribute.class);

    llvm::ArrayRef<std::pair<ValueRange, Type>> replicated_inputs,
    ValueRange packed_inputs, TypeRange replica_output_types) {
  DCHECK_GE(n, 2);
  state->addAttribute("n", builder->getI32IntegerAttr(n));

  if (devices.has_value()) state->addAttribute("devices", devices.value());

  Region* region = state->addRegion();
  region->push_back(new Block);
  Block& block = region->front();

    Block &GetBody() { return getOperation()->getRegion(0).front(); }
    bool WrapsSingleOp();
  }];

  let builders = [
    OpBuilder<(ins "StringAttr":$device, "TypeRange":$result_types),
    [{
      $_state.addAttribute("device", device);
      $_state.addTypes(result_types);
      $_state.addRegion();
    }]>
  ];

  let hasCanonicalizer = 1;
}

        if (trimSpaceEnabled) {
            final Matcher matcher = SPACE_PATTERN.matcher(value);
            return matcher.replaceAll(" ").trim();
        }
        return value;
    }

    public void addAttribute(final String name, final Object value) {
        attributeMap.put(name, value);
    }

    public void addFeature(final String key, final String value) {
        featureMap.put(key, value);
    }

                            result.attributes))
    return failure();

  Type i64 = parser.getBuilder().getIntegerType(64);
  if (parser.parseOptionalKeyword("parallel_iterations")) {
    result.addAttribute("parallel_iterations",
                        IntegerAttr::get(i64, kDefaultParallelIterations));
  } else {
    IntegerAttr parallel_iterations;

  let results = (outs TFR_AttrType:$out);

  let hasFolder = 1;

  let builders = [
    OpBuilder<(ins "Attribute":$value),
    [{
      auto* ctx = value.getContext();
      $_state.addAttribute("value", value);
      $_state.addTypes(TFRAttrType::get(ctx));
    }]>
  ];

  let assemblyFormat = [{
    $value attr-dict `->` type($out)
  }];
}

        Type output_type;
        TF_RETURN_IF_ERROR(AddRef(type, &output_type));
        type = output_type;
      }
    }
  }
  for (auto& it : attrs_) state_->addAttribute(it.first(), it.second);
  *state = state_.get();
  return absl::OkStatus();
}

const string& MlirAbstractOp::Name() const { return tf_op_type_; }