declaration nodes) in the future.

If you want to connect a new low-level compiler, you would create a new dialect
and the lowerings between the TensorFlow Graph dialect and your dialect.
This smooths the path for hardware and compiler makers. You can even target
dialects at different levels in the same model; the higher-level optimizers
will respect the unfamiliar parts of the IR and wait for a lower level to handle

      }
      if (dialect != current_dialect) {
        if (!dialects.contains(current_dialect)) {
          wrapOpsInFunction(ops, counter++, module);
        }
        ops.clear();
        current_dialect = dialect;
      }
      ops.push_back(&op);
    }
    if (!dialects.contains(current_dialect)) {
      wrapOpsInFunction(ops, counter++, module);
    }
  }
}

void GroupByDialectPass::runOnOperation() {

    fi
    cd ..
  fi
fi


for dialect in "${dialects[@]}" ; do
  if [ "$GORM_DIALECT" = "" ] || [ "$GORM_DIALECT" = "${dialect}" ]
  then
    echo "testing ${dialect}..."

    if [ "$GORM_VERBOSE" = "" ]
    then
      GORM_DIALECT=${dialect} go test -race -count=1 ./...
      if [ -d tests ]
      then
        cd tests
        GORM_DIALECT=${dialect} go test -race -count=1 ./...
        cd ..
      fi

}
```

## TensorFlow Executor Dialect

The `tf_executor` dialect is intended to model the current TensorFlow executor
semantics and (when combined with the `tf` dialect) can represent arbitrary
TensorFlow 1.x and 2.x graphs. As such it follows the executor model, including
deadness propagation, concurrent semantics, and control dependencies. The
`tf_executor` dialect defines two dialect-specific types:

#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/Extensions/AllExtensions.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Quant/QuantOps.h"  // from @llvm-project
#include "mlir/Dialect/SCF/IR/SCF.h"  // from @llvm-project
#include "mlir/Dialect/Shape/IR/Shape.h"  // from @llvm-project

#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/ControlFlow/IR/ControlFlow.h"  // from @llvm-project
#include "mlir/Dialect/Func/Extensions/AllExtensions.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/MLProgram/IR/MLProgram.h"  // from @llvm-project

#include "mlir/IR/Dialect.h"  // from @llvm-project
#include "mlir/IR/OpDefinition.h"  // from @llvm-project
#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/Interfaces/ControlFlowInterfaces.h"  // from @llvm-project
#include "mlir/Interfaces/SideEffectInterfaces.h"  // from @llvm-project

namespace mlir {
namespace tf_device {

// The TensorFlow Device dialect.
//

#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/Extensions/AllExtensions.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Quant/QuantOps.h"  // from @llvm-project
#include "mlir/Dialect/Quant/QuantTypes.h"  // from @llvm-project
#include "mlir/Dialect/SCF/IR/SCF.h"  // from @llvm-project

namespace tensorflow {
namespace tf2xla {
namespace v2 {

// Given the input Module op that's in the Tensorflow Dialect, convert the MLIR
// module in place to the Tensorflow Executor Dialect. Returns an OK Status if
// success, otherwise failure with an error message.
// The Tensorflow Executor Dialect is required to export an MLIR module to a
// Tensorflow GraphDef. This API will add control dependencies and verify that

- Raise to Control-flow-graph. Converts TF Control Flow dialect to TF dialect.
- The Canonicalization pass iteratively applies canonicalization
transformations in a greedy way until no further changes occur.
Canonicalization includes constant folding.
- The Legalize pass converts TensorFlow operations to TensorFlow Lite
ones. The operations that cannot be mapped to TensorFlow Lite dialect