#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 {

# For e.g. this file will be originally placed under tensorflow/_api/v1 which
# does not have "python", "core" directories. Then, it will be copied
# to tensorflow/ which does have these two directories.

try:
  del python
except NameError:
  pass
try:
  del core
except NameError:
  pass
try:
  del compiler
except NameError:
  pass

namespace tflite {

// Convert a model represented in `input_contents`. `model_flags_proto`
// describes model parameters. `flags_proto` describes conversion
// parameters (see relevant .protos for more information). Returns a string
// representing the contents of the converted model. When extended_return
// flag is set to true returns a dictionary that contains string representation

      auto entrypoint = entrypoints.begin()->second;
      Builder builder(entrypoint);
      entrypoint.setName(builder.getStringAttr("main"));
      return;
    }

    // In case we have more than 1 entry points, choose the one with
    // 'tf.entry_function' attribute set.
    llvm::SmallVector<func::FuncOp, 4> candidate_funcs;
    for (auto& entrypoint : entrypoints) {

    ],
    deps = [
        ":modular_filesystem",
        "//tensorflow/core:framework_internal",
        "//tensorflow/core/lib/io:path",
        "//tensorflow/core/platform:env",
        "//tensorflow/core/platform:errors",
        "//tensorflow/core/platform:stacktrace_handler",
        "//tensorflow/core/platform:test",
    ],

  return success();
}

LogicalResult VerifyNgrams(func::FuncOp func) {
  // The inputs and outputs should be the same:
  // * A string tensor for tokens/ragged tensor values.
  // * Zero or more row_split tensors.
  constexpr int kValues = 0;
  constexpr int kRowSplits = 1;

  if (func.getFunctionType().getInputs().size() !=
      func.getFunctionType().getResults().size()) {

  }

  VLOG(1) << "Run MLIR Graph Optimization Passes";
  PassManager pm(module.getContext());
  ::tensorflow::applyTensorflowAndCLOptions(pm);

  // Run island coarsening before shape inference to allow more exact shape
  // inference using constant folding within islands.
  pm.addNestedPass<func::FuncOp>(
      tf_executor::CreateTFExecutorIslandCoarseningPass());
  pm.addPass(CreateTFShapeInferencePass());

#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/tensor_util.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/platform/bfloat16.h"

// opinions taken by that codebase (vs providing any amount of genericity).
//
// Specifically, it combines the following concerns, each of which would be
// independent variables in a more generic setup:
//   - numBits and isSigned imply storage data type (uint8, int8, int16)
//   - numBits < 8 is promoted to uint8 or int8
//   - "narrow_range" narrows the lower bound of the storage type's range by
//     1

  // If true, always print the top-level operation when printing IR for
  // print_ir_[before|after].
  optional bool print_ir_module_scope = 7 [default = true];

  // Elide ElementsAttrs with \"...\" that have more elements than the given
  // upper limit.
  optional int64 elide_elementsattrs_if_larger = 8;