config.mutable_calibration_options()
        ->mutable_representative_datasets()
        ->Add(preset_datasets.begin(), preset_datasets.end());
  }

  // Create a new `QuantizationSpecs` to replace the existing one. The
  // expansion from `StaticRangePtqPreset` gets populated first and then
  // user-provided explicit `QuantizationSpec`s will be appended.
  QuantizationSpecs new_specs{};

                                    /*allow_partial_conversion=*/false);

  auto pass_instrumentors = mlir::GetPassInstrumentors();
  for (const auto& creator : pass_instrumentors) {
    tf2xla.addInstrumentation(creator());
  }
  if (DEBUG_DATA_DUMPER()->ShouldDump(module_name.str(), kDebugGroupMain) ||
      VLOG_IS_ON(1)) {
    tensorflow::DumpMlirOpToFile(

    return convert(attr);
  }
  return nullptr;
}

DenseElementsAttr UniformQuantizedPerAxisValueConverter::convert(
    DenseFPElementsAttr attr) {
  // Creates the converter for each chunk. Normally the size of the
  // quantization dim is 3, so we can cache all the converters.
  ShapedType type = attr.getType();
  std::size_t dim_size = type.getDimSize(quantization_dim_);

    ModuleOp module, llvm::ArrayRef<llvm::StringRef> function_names) {
  SymbolTableCollection symbol_table;
  OpBuilder builder(module.getContext());

  OwningOpRef<ModuleOp> pruned =
      builder.create<ModuleOp>(module->getLoc());
  (*pruned)->setAttrs(module->getAttrs());
  builder.setInsertionPointToEnd(pruned->getBody());

  llvm::SmallDenseSet<func::FuncOp> added;

#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/platform/errors.h"

using tensorflow::tracing::MaybeSetOpName;

namespace tensorflow {
namespace ops {

// Op: VarHandleOp()
// Summary: Creates a handle to a Variable resource.
//
// Description:
Status VarHandleOp(AbstractContext* ctx, AbstractTensorHandle** resource,
                   DataType dtype, const PartialTensorShape shape,

  for (int i = 0; i < y_num; i++) {
    dy_datas[i] = Tensor(ys[i].type(), y_shapes[i]);
    auto dy_data_flat = dy_datas[i].flat<Y_T>();
    dy_data_flat.setZero();
  }

  // Create the feed list.
  ClientSession::FeedType feed_list;
  for (int i = 0; i < x_num; i++) {
    feed_list.insert({xs[i], x_datas[i]});
  }
  for (int i = 0; i < y_num; i++) {
    feed_list.insert({dys[i], dy_datas[i]});

    compilation_successful.scalar<bool>()() = false;
    ctx->set_output(0, compilation_key);
    ctx->set_output(1, compilation_successful);
    return;
  }

  // Each execution of an XlaCompile op creates a new ExecutableClosure, even
  // if it didn't have to compile the cluster because of a compilation-cache
  // hit.  This is because we at least need new snapshots of the resource
  // variables.

        dyn_cast<arith::ConstantOp>(op.getOperand(0).getDefiningOp());
    if (!input_op) {
      return failure();
    }

    Builder builder(op.getContext());
    auto new_gather_op = rewriter.create<TFL::GatherOp>(
        op.getLoc(),
        /*result=*/
        mlir::cast<TensorType>(op.getResult().getType())
            .clone(builder.getI4Type()),
        /*operand=*/op.getOperands(), op->getAttrs());

                            assign.getValue().getType());
  }

  OpBuilder builder(replicate_op);
  // Clone this old replicate op but with new result types.
  auto new_replicate_op = builder.create<tf_device::ReplicateOp>(
      replicate_op->getLoc(), new_result_types, replicate_op->getOperands(),
      replicate_op->getAttrs());

  // Move region to the new op.

                          std::vector<Output>* grad_outputs);

  // Returns a list mapping whether each node in the graph is reachable
  // from outputs_. Keyed by node id.
  std::vector<bool> GetReachableNodes();

  // Creates the gradient subgraph for a while loop (or just stores
  // `summed_grads` if not all incoming gradients are available yet). All exit
  // nodes (which are the first nodes of a loop encountered in the backwards