// Additionally, we take advantage of the fact that the SignatureDefFunction's
// associated functiondef has lexicographically ordered inputs/outputs due to
// nest.flatten.
Status LoadSignatureDefFunctionMetadata(
    const SavedConcreteFunction& saved_concrete_function,
    SignatureDefFunctionMetadata* out) {
  std::vector<SignatureDefParam> args;

  auto indices_size = ExpandDims(scope, Size(scope, indices), 0);
  Output outer_shape, flat_values_shape;
  if (batch_dims != 0) {
    auto values_shape = Shape(scope, values);
    // Add the batch offsets to indices and flatten the batch dimensions.
    outer_shape = Slice(scope, values_shape, {0}, {batch_dims});
    auto inner_shape =
        Slice(scope, Slice(scope, values_shape, {batch_dims}, {-1}), {1}, {-1});

      rhs,
      DenseIntElementsAttr::get(
          RankedTensorType::get({rhs_rank}, rewriter.getI64Type()),
          rhs_permutation));
  // Reshapes lhs to flatten out_dimensions and contracting_dimensions.
  llvm::SmallVector<int64_t, 4> lhs_flattened_shape = Concat<int64_t>(
      lhs_dot_dimensions_info.batch_dimensions().SizesArray(),
      llvm::ArrayRef<int64_t>{

    llvm::StringRef equation, RankedTensorType lhs_ty) {
  llvm::StringRef lhs;
  llvm::StringRef out;
  std::tie(lhs, out) = equation.split("->");
  if (lhs.empty() || out.empty()) return std::nullopt;

  // Try to flatten the "..." if possible.
  int lhs_named_label, rhs_named_label;

  // following rhs and rhs_ty variables are non-functional here only created to
  // comply with the existing API
  llvm::StringRef rhs;

    const SavedConcreteFunction& saved_concrete_function,
    const FunctionDef* function_def) {
  // tf.functions go through many transformations before becoming FunctionDefs
  // 1. flatten user-provided inputs:
  // https://github.com/tensorflow/tensorflow/blob/1c064ab76064c58e54261b805027474885a1534d/tensorflow/python/eager/function.py#L2671-L2675
  // 2. convert user-provided inputs to tensors:

  // Add inline pass.
  pass_manager.addPass(mlir::createInlinerPass());

  // Expands mhlo.tuple ops.
  pass_manager.addPass(
      mlir::mhlo::createExpandHloTuplesPass(entry_function_name.str()));
  // Flatten tuples for control flows.
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::mhlo::createFlattenTuplePass());

  mlir::odml::AddMhloOptimizationPasses(
      pass_manager,

}

// Determine if op commutes with transposes. Requires a strict
// definition of Elementwise, all i/o shapes and types must be same-rank
// broadcastable and fully static. Consider moving this into attribute later.
bool IsElementwise(Operation *op) {
  if (!(llvm::isa<TFL::AddOp, TFL::MulOp, TFL::DivOp, TFL::SubOp,
                  TFL::MaximumOp, TFL::MinimumOp>(op))) {
    return false;
  }

  auto opr1_type =

  // Guarantee all functions have one use, which enables more exact shape
  // inference.
  pm.addPass(mlir::TF::CreateGuaranteeAllFuncsOneUsePass());
  // Run shape inference so that tf_executor/tf_device ops created later will
  // likely to inherit more concrete types.
  pm.addPass(mlir::TF::CreateTFShapeInferencePass());
  pm.addNestedPass<FuncOp>(mlir::TFTPU::CreateTPUPartitionedOpConversionPass());
  pm.addNestedPass<FuncOp>(

// -------------------------------------------------------------------------- //

// Clustering policy specifies if the operation can be clustered (in practice it
// usually means that operation can be added to a cluster that will be later
// compiled) given the set of constraints on its results, and might propagate or
// create new constraints on the operation operands.
//
// Clustering policy must make a local decision just for a single operation. It

    for (const int itensor : operations_[ioperation].tensors) {
      if (const Tensor& tensor = tensors_[itensor];
          tensor.first_use() == ioperation /* output */ &&
          tensor.last_use() > peak_loc /* used later */) {
        max_savings += tensor.size;
      }
    }
  }
  return max_savings;
}

std::tuple<Rematerializer::SizeT, Rematerializer::RematSpec>