llvm::raw_string_ostream os{error_message};
          os << "The argument with type ";
          arg.getType().print(os);
          os << " should have one user, which should be tfr.cast.";
          func->emitError(error_message);
          return;
        }
      }
    }

    builder.setInsertionPoint(terminator);
    // Replace tfr.cast(tensor<quant_type>) -> output
    // with tfr.cast(tensor<storage_type>) -> output

  // Apply patterns to reachable functions.
  for (Operation* op : reachable_functions) {
    assert(isa<func::FuncOp>(op));
    if (failed(applyPatternsAndFoldGreedily(op, patterns))) {
      return op->emitError() << kBadDecompositionMessage;
    }
  }

  // Apply patterns to device cluster ops.
  // Note: This module search for cluster ops is a bit wasteful as we could have

                            MLIRContext* context, ArrayRef<StringAttr> attrs) {
    return Base::getChecked(emitError, context, attrs);
  }

  static Derived get(MLIRContext* context) { return get({}, context); }

  // TODO(fengliuai): fix the implementation
  static LogicalResult verify(function_ref<InFlightDiagnostic()> emitError,

          absl::StrCat("Node `", op->getName().getStringRef().str(), "` ",
                       "is a launch op which should have been removed by "
                       "outside compilation");

      op->emitError() << launch_error;
      LOG(ERROR) << launch_error;
      return WalkResult::interrupt();
    }

    if (HasXlaOutsideCompilationMarker(*op)) {
      std::string outside_compilation_error = absl::StrCat(

  if (!(tensor_type && tensor_type.hasRank())) {
    return op->emitError()
           << "cannot convert op with unranked or non-tensor input type "
           << tensor_type << ".";
  }

  int rank = tensor_type.getRank();
  if (rank <= partition_dim) {
    return op->emitError() << "cannot partition " << first_operand_type
                           << " (rank = " << rank << ") along dimension "

            sharding)
            .failed()) {
      return cluster_func.emitError("incorrect sharding format for inputs");
    }

    const auto input_sharding_type = sharding.type();

    auto tiled_sharding_mismatched = [&](int tiled_input_size) {
      return cluster_func.emitError(
          llvm::formatv("incorrect {0}-th tiled input sharding received. "

  attr.getValue().split(attr_tokens, ",");
  if (attr_tokens.empty()) {
    return fused_func_op_.emitError()
           << kTFImplements << " attribute should be set";
  }

  // Check if the interface matches.
  if (GetCompositeOpName().str() != attr_tokens[0]) {
    return fused_func_op_.emitError()
           << "Unexpected interface for the composite op. Expected: "

      host_module.str(), op->getContext(), &module_for_func);
  if (!status.ok()) {
    return op.emitError()
           << "attribute 'host_mlir_module' can not be deserialized. "
           << status.message();
  }

  func::FuncOp func = module_for_func->lookupSymbol<func::FuncOp>("host_func");
  if (!func)
    return op.emitError()
           << "serialized module in attribute 'host_mlir_module' does not "

  auto result_type =
      OpTrait::util::getBroadcastedType(x.getType(), y.getType());
  if (!result_type) {
    if (incompatible_shape_error.getValue()) {
      mlir::emitError(loc, "non-broadcastable operands");
    } else {
      return UnrankedTensorType::get(builder->getI1Type());
    }
  }

  auto ranked_type = mlir::dyn_cast<RankedTensorType>(result_type);

                          " which is not an accepted dialect";
      op->emitError() << error;
      return WalkResult::interrupt();
    }

    if (IsTfDeviceClusterFuncOp(op)) {
      std::string error =
          "failed TF functional to executor validation, op "
          "tf_device.cluster_func is not allowed";
      op->emitError() << error;
      return WalkResult::interrupt();
    }