@parameterized.parameters(
      testing.parameter_combinations([{
          'bias_fn': (
              None,
              nn_ops.bias_add,
          ),
          'activation_fn': (
              None,
              nn_ops.relu,
              nn_ops.relu6,
          ),
          'dim_sizes': (
              # tf.MatMul cases.
              ([None, 1024], [1024, 3]),  # dynamic batch dim.

    const Tensor& cc_b, Tensor* cc_out,
    void (*binary_add_func)(TF_OpKernelContext* ctx, TF_Tensor* a, TF_Tensor* b,
                            TF_Tensor* out)) {
  if (cc_a.dtype() == ::tensorflow::DT_INVALID) {
    *cc_out = cc_b;
    return absl::OkStatus();
  }
  if (cc_b.dtype() == ::tensorflow::DT_INVALID) {
    *cc_out = cc_a;
    return absl::OkStatus();
  }

  Status status;

)

exports_files([
    "ir/tf_generated_ops.td",
    "ir/tf_op_base.td",
    "ir/tf_op_interfaces.td",
    "ir/tf_ops.td",
])

td_library(
    name = "tensorflow_ops_td_files",
    srcs = [
        "ir/tf_generated_ops.td",
        "ir/tf_op_base.td",
        "ir/tf_op_interfaces.td",
        "ir/tf_ops.td",
    ],
    compatible_with = get_compatible_with_portable(),
    deps = [

        mean, variance = scale, offset
        out = nn_ops.conv2d(
            input_tensor,
            self.filters,
            strides=strides,
            dilations=dilations,
            padding=padding,
            data_format='NHWC',
            name='sample/conv',
        )
        if bias_fn is not None:
          out = nn_ops.bias_add(out, self.bias)
        if has_batch_norm:

limitations under the License.
==============================================================================*/

include "mlir/IR/OpBase.td"
include "mlir/IR/PatternBase.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"

// TODO(jpienaar): Move this somewhere general.
class GetI64ScalarElementsAttr<int value> :
  NativeCodeCall<"DenseElementsAttr::get<int64_t>(RankedTensorType::get({}, $_builder.getIntegerType(64)), " # value # ")">;

#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir::quant::stablehlo {

#define GEN_PASS_DEF_XLACALLMODULETOCALLPASS
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/passes.h.inc"

namespace {

#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace TF {

class ResourceAnalyzer {
 public:
  explicit ResourceAnalyzer(ModuleOp module, bool skip_session_init = false);

  bool IsPotentiallyWritten(Value resource) const;

#include "mlir/IR/Visitors.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Support/TypeID.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/legalization_op_config.h"
#include "tensorflow/core/lib/monitoring/counter.h"

namespace tensorflow {
namespace tf2xla {
namespace internal {

namespace {

#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/utils/variables_utils.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"

namespace mlir {
namespace TFL {
namespace {

def LegalizeCast : Pat<(TF_CastOp $arg0, BoolAttr:$arg1), (TFL_CastOp $arg0)>;

def LegalizeBatchToSpaceND : Pat<
  (TF_BatchToSpaceNDOp $input, $block_shape, $crops),
  (TFL_BatchToSpaceNdOp $input, (CreateTFCastToInt32Op $block_shape),
     (CreateTFCastToInt32Op $crops))>;

def LegalizeSpaceToBatchND : Pat<
  (TF_SpaceToBatchNDOp $input, $block_shape, $paddings),
  (TFL_SpaceToBatchNdOp $input, (CreateTFCastToInt32Op $block_shape),