func.return %conv2d : tensor<1x300x300x40xi8>
}

//===----------------------------------------------------------------------===//
// tf.UniqueV2 legalization
//===----------------------------------------------------------------------===//

// -----

// CHECK-LABEL: @doesnt_legalize_uniquev2

  // it is disabled explicitly.
  bool infer_tensor_range =
      (quant_specs_.post_training_quantization || eager_quantize) &&
      !quant_specs_.disable_infer_tensor_range;

  // During the legalization, unsigned quantized type is used, so we have to
  // convert all of them to signed.
  RewritePatternSet patterns(ctx);
  populateWithGenerated(patterns);

    patterns_1.add<PropagateTransposedPerAxisQuantDim>(ctx);
  }
  (void)applyPatternsAndFoldGreedily(func, std::move(patterns_1));

  // During the legalization, unsigned quantized type is used, so we have to
  // convert all of them to signed.
  RewritePatternSet patterns_2(&getContext());
  if (is_signed) {

    // Replace the uses of block arguments with function arguments. Note that we
    // can't erase the arguments here because the operations may still use them
    // and these uses will be dropped after legalization of each op.
    unsigned idx = 0;
    Block &block = graph_func.getBody().front();
    for (auto iter = block.args_begin(), end_iter = block.args_end();
         iter != end_iter; ++iter) {

// Note that binary elementwise tests are run with chlo legalization enabled
// (unlike the rest), since this is the primary use case for such ops and
// verification of shapes and broadcasts is desired.
// RUN: tf-opt "-xla-legalize-tf=legalize-chlo=true" -canonicalize %s | FileCheck %s
// RUN: tf-opt "-xla-legalize-tf=legalize-chlo=false" %s | FileCheck --check-prefix CHLO %s

//===----------------------------------------------------------------------===//

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// This is the legalization pattern definition file for TF to XLA.

include "mlir/IR/OpBase.td"
include "mlir/Dialect/Shape/IR/ShapeOps.td"
include "mlir/Dialect/Func/IR/FuncOps.td"
include "mlir/Dialect/Tensor/IR/TensorOps.td"

  return op->getResult(0).getType().cast<TensorType>().clone(
      new_result_quantized_type);
}

// Matches kernel dimension numbers, ranks of input and output and constant
// kernel for legalization to TFLite convolution ops.
LogicalResult MatchConvolutionFormat(stablehlo::ConvolutionOp op) {
  stablehlo::ConvDimensionNumbersAttr dimension_numbers =
      op.getDimensionNumbers();

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// This is the legalization pattern definition file for HLO to TF.

include "mlir/IR/OpBase.td"
include "mlir/Dialect/Func/IR/FuncOps.td"
include "stablehlo/dialect/ChloOps.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// TFLite legalization patterns

include "mlir/IR/OpBase.td"
include "mlir/IR/PatternBase.td"
include "mlir/Dialect/Arith/IR/ArithOps.td"
include "mlir/Dialect/Func/IR/FuncOps.td"
include "tensorflow/compiler/mlir/lite/ir/tfl_ops.td"

    }
    os << "    if (!emit_error_on_verify_fail) {\n";
    os << "// Ignore transient errors by registering an no-op handler.\n"
          "// Applying legalization patterns will emit unwanted, transient \n"
          "// errors when the replaced TFLite ops do not meet the sanity \n"
          "// checks. \n"
          "// In order to ignore the transient errors, the following lines \n"