void AddPostQuantizationStableHloToTfPasses(
    const mlir::TFL::PassConfig& pass_config,
    mlir::OpPassManager& pass_manager);

// This is the early part of the conversion in isolation. This enables a caller
// to inject more information in the middle of the conversion before resuming it
// (like freezing variables for example).
void AddPreVariableFreezingTFToTFLConversionPasses(
    const mlir::TFL::PassConfig& pass_config,

#include <memory>
#include <string>

#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/util/tensor_bundle/tensor_bundle.h"
#include "tensorflow/core/util/tensor_slice_reader.h"

namespace tensorflow {
namespace checkpoint {

class TensorSliceReader;

                             absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params);

// A callable that takes tensor inputs and returns zero or more tensor outputs.
using Model = std::function<Status(AbstractContext*,
                                   absl::Span<AbstractTensorHandle* const>,
                                   absl::Span<AbstractTensorHandle*>)>;

// will fail. If `prefix` is nullptr, the default prefixing behaviour takes
// place, see TF_AddGradients for more details.
//
// WARNING: This function does not yet support all the gradients that python
// supports. See
// https://www.tensorflow.org/code/tensorflow/cc/gradients/README.md
// for instructions on how to add C++ more gradients.
TF_CAPI_EXPORT void TF_AddGradientsWithPrefix(TF_Graph* g, const char* prefix,

// Permutation from the OIHW (== (output features, input features, height,
// width)) tensor format to HWIO. This is commonly used to transpose convolution
// weights represented as OIHW format to HWIO, which is more desirable for
// certain downstream optimization passes (e.g. XLA).
inline constexpr std::array<int64_t, 4> kOihwToHwioPermutation = {2, 3, 1, 0};

// Returns true if the value has static shape.

#include "tensorflow/compiler/mlir/quantization/tensorflow/exported_model.pb.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/python/lib/core/pybind11_lib.h"
#include "tsl/platform/protobuf.h"  // IWYU pragma: keep

namespace pybind11::detail {
namespace internal {

// `base_loc` is used for error reporting and debug info.
// If ordered_output_arrays is not empty, then the imported mlir function will
// only return nodes in ordered_output_arrays in the same order. Returns nullptr
// on failure, and more specific errors will be emitted via the context.
// If `use_external_constant` is true, it will create `tfl.external_const`
// instead of `tfl.const`.
// If `experimental_prune_unreachable_nodes_unconditionally` is true, nodes that

// PlatformDeviceId if a mapping between between TfDeviceId and PlatformDeviceId
// is set; otherwise returns the id in the device name. Please refer to
// tensorflow/tsl/framework/device_id.h for more details.
// For mobile or slim build, returns the id in the device name.
TF_CAPI_EXPORT extern int TF_GetDeviceId(TF_OpKernelContext* ctx);

// Returns the Device Name of the device that the context possesses.
//

// converting them to NHWC formats along with extra optimizations such as
// constant folding the transpose->convolution pattern. This is useful when
// downstream pipeline (e.g. XLA) is more optimized when accepting NHWC formats.
void AddProcessNchwTensorPasses(OpPassManager& pm);

// Registers quantization pass pipelines. This is only required when running

      // TODO(b/172517537): check if other tensors should go through this
      // check too.
      op.emitError() << "Input tensor [" << input_index
                     << "] is a state tensor, but has more than one use.";
      return failure();
    }
    auto stats = mlir::dyn_cast<DenseFPElementsAttr>(stats_op.getLayerStats());
    if (!stats || stats.getNumElements() != 2) {