def IsDataFormatNHWC : ConstantAttr<TF_ConvnetDataFormatAttr, "\"NHWC\"">;

// Get the last dimension size as a 1-d single element attr.
def GetLastDimSizeAsI32 : NativeCodeCall<
  "DenseElementsAttr::get(RankedTensorType::get({1}, $_builder.getIntegerType(32)), "
  "static_cast<int32_t>($0.getType().cast<RankedTensorType>().getDimSize(  "
  "  $0.getType().cast<RankedTensorType>().getRank() - 1)))">;

                              shape_determination_fns, compilation_result);
}

static mlir::LogicalResult MlirTfToHloTextTranslateFunctionImpl(
    mlir::ModuleOp module_op, llvm::raw_ostream& output, bool via_builder) {
  if (!module_op) return mlir::failure();

  llvm::SmallVector<TensorOrResourceShape, 4> arg_shapes;
  auto args_status =
      ParseArgumentShapes(mlir::StringRefToView(input_shapes), arg_shapes);

`tf_device` dialect operation.
  }];

  let arguments = (ins
    Variadic<AnyType>:$results
  );

  let builders = [
    OpBuilder<(ins),
    [{
      build($_builder, $_state, {});
    }]>
   ];

  let assemblyFormat = "attr-dict ($results^ `:` type($results))?";
}

def TfDevice_LaunchFuncOp : TfDevice_Op<"launch_func", []> {
  let summary = [{

        tensors. Each tensor contains the asset file names.
    """
    v1_builder = builder.SavedModelBuilder(saved_model_path)
    sig_def = signature_def_utils_impl.predict_signature_def(
        inputs=inputs, outputs=outputs
    )

    v1_builder.add_meta_graph_and_variables(
        sess,
        tags,
        signature_def_map={signature_key: sig_def},

   (HasNonNegativeOffset $begin, $end)]>;

def GetNumElementsOrOne: NativeCodeCall<"GetNumElementsOrOne($0.getType())">;

def ReshapeValueDroppingLastDim : NativeCodeCall<
  "ReshapeValueDroppingLastDim($_builder, $0)">;

def IsOneHotIndexAttribute : Constraint<CPred<
  "TFL::IsOneHotIndexAttribute($0)">>;

// Checks if the shape has static shape with last dimension equals 1.

    "StringAttr":$fusedActivationFunction),
  [{
    buildFusedBroadcastableBinOp(
       &$_builder, $_state, lhs, rhs, fusedActivationFunction);
  }]>;

def TFL_ComparisonBinaryBuilder :
  OpBuilder<(ins "Value":$lhs, "Value":$rhs),
  [{
    buildComparisonBinOp(&$_builder, $_state, lhs, rhs);
  }]>;

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

// tensor argument types
class TFR_Type<string name> : DialectType<TFR_Dialect,
    CPred<"$_self.isa<mlir::TFR::" # name # "Type>()">,
    "TFR " # name #" type">,
    BuildableType<"$_builder.getType<mlir::TFR::" # name # "Type>()">;
def TFR_TensorType : TFR_Type<"TFRTensor">;
def TFR_TensorListType : TFR_Type<"TFRTensorList">;
def TFR_AllTensorTypes : Type<Or<[
    TFR_TensorType.predicate,

      )
      signature_map = {'main': signature}

      tags = {tag_constants.SERVING}
      v1_builder = builder.SavedModelBuilder(self._input_saved_model_path)
      v1_builder.add_meta_graph_and_variables(
          sess, tags, signature_def_map=signature_map
      )
      v1_builder.save()

    quantization_options = quant_opts_pb2.QuantizationOptions(

	}

	if argLen > sys.ExecArgLengthLimit {
		return true
	}

	// On the Go build system, use response files about 10% of the
	// time, just to exercise this codepath.
	isBuilder := os.Getenv("GO_BUILDER_NAME") != ""
	if isBuilder && rand.Intn(10) == 0 {
		return true
	}

	return false
}

// encodeArg encodes an argument for response file writing.
func encodeArg(arg string) string {

      "llvm::ArrayRef<::mlir::NamedAttribute>":$attributes,
      "unsigned":$numRegions),
    [{
      assert(numRegions == 2u && "mismatched number of regions");
      build($_builder, $_state, resultTypes, operands, attributes);
    }]>];

  let hasCanonicalizer = 1;
}

def TF_GeneratorDatasetRegionOp : TF_Op<"GeneratorDatasetRegion",
      [AttrSizedOperandSegments,