ASSERT_THAT(failed(), Not(IsOkOrFiltered()));
}

TEST(TestUtil, MatchesFiltered) { ASSERT_THAT(filtered(), IsOkOrFiltered()); }

TEST(TestUtil, IncrementsOk) {
  CellReader<int64_t> reader(kMetric);
  counter->GetCell(kOkStatus)->IncrementBy(1);

  ASSERT_THAT(success(), IncrementedOrFiltered(reader.Delta(kOkStatus), 1));
}

TEST(TestUtil, FilteredDoesntIncrementsOk) {
  CellReader<int64_t> reader(kMetric);

      "VarHandleOp",       // b/176819198
  };
  for (const char* skip : kOpsToSkip) {
    if (absl::StartsWith(orig_op->op_name(), skip)) return absl::OkStatus();
  }

  tf_core_op_expansion_node_counter->GetCell()->IncrementBy(1);

  LOG_FIRST_N(INFO, 1) << "Run Node Expansion Passes";

  // Get the FunctionDef and insert that into the context
  const NodeDef& ndef = orig_op->MutableAttrs()->BuildNodeDef();

    std::vector<std::vector<xla::Shape>>* per_core_arg_shapes,
    xla::CompileOnlyClient* client) {
  CompilationTimer timer;
  auto record_time = llvm::make_scope_exit([&timer] {
    phase2_bridge_compilation_time->GetCell(kFullBridge)
        ->Add(timer.ElapsedCyclesInMilliseconds());
  });

  auto compilation_result = std::make_unique<XlaCompilationResult>();

  DumpComputationInput(computation);

  }

  void runOnOperation() override;
};

static void IncrementCounterFor(tensorflow::monitoring::Counter<1>* counter,
                                Operation* op) {
  counter->GetCell(op->getName().getStringRef().str())->IncrementBy(1);
}

bool HasBounds(RankedTensorType type) {
  auto encoding = mlir::dyn_cast_or_null<mlir::mhlo::TypeExtensionsAttr>(
      type.getEncoding());

        disable_set_input_nodes_quantization_params_;
  }

  if (quant_specs_.post_training_quantization) {
    tflite_quantizer_usage_stats->GetCell("post_training")->IncrementBy(1);
    RemoveRedundantStats(func);
  } else {
    tflite_quantizer_usage_stats->GetCell("during_training")->IncrementBy(1);
    // Set the quantization parameters for the quantizable input nodes. If this

        return getCell(index);
      }
    };
  }

  @Override
  Spliterator<Cell<R, C, @Nullable V>> cellSpliterator() {
    return CollectSpliterators.<Cell<R, C, @Nullable V>>indexed(
        size(), Spliterator.ORDERED | Spliterator.NONNULL | Spliterator.DISTINCT, this::getCell);
  }

  private Cell<R, C, @Nullable V> getCell(final int index) {

        return failure();
    }
    rewriter.replaceOp(op, rewriter.create(state)->getResults());

    // TODO: b/290366702 - Temporarily added metrics for debugging.
    mlir_tf_quant_op_count->GetCell(std::string(op->getName().getStringRef()))
        ->IncrementBy(1);
    return success();
  }
};

// This pattern adds qint <-> int Cast to all qint operands and results for UQ
// ops.

    return new AbstractIndexedListIterator<Cell<R, C, @Nullable V>>(size()) {
      @Override
      protected Cell<R, C, @Nullable V> get(final int index) {
        return getCell(index);
      }
    };
  }

  private Cell<R, C, @Nullable V> getCell(final int index) {
    return new Tables.AbstractCell<R, C, @Nullable V>() {
      final int rowIndex = index / columnList.size();

                                "The number of composite op expanded.", "name");
}

void IncreaseOpExpansionExecuteCounterByOne(const std::string& op_name) {
  tf_core_op_expansion_op_counter->GetCell(op_name)->IncrementBy(1);
}

}  // namespace tensorflow

//===----------------------------------------------------------------------===//
// The pass to decompose unregistered TF ops with the TFR compose function.
//

                          .str()));
      outside_compiled_cluster_counter++;
    }
  });
  if (outside_compiled_cluster_counter > 0) {
    auto_outside_compilation_gauge->GetCell()->Set(true);
  }
  return mlir::success();
}

// Check for uncompilable ops that are in `tf_dialect` and are not already
// marked for outside compilation.