if (!expand_op.getResult().hasOneUse()) {
      return rewriter.notifyMatchFailure(
          expand_op, "result for current op has more than 1 use");
    }
    if (!squeeze_op.getResult().hasOneUse()) {
      return rewriter.notifyMatchFailure(
          squeeze_op, "result for current op has more than 1 use");
    }
    // Make sure that the axis in `expand_op` is constant.
    if (auto const_op =

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

      : filesystem_(std::move(filesystem)),
        ops_(std::move(filesystem_ops)),
        random_access_file_ops_(std::move(random_access_file_ops)),
        writable_file_ops_(std::move(writable_file_ops)),
        read_only_memory_region_ops_(std::move(read_only_memory_region_ops)),
        plugin_memory_allocate_(std::move(plugin_memory_allocate)),
        plugin_memory_free_(std::move(plugin_memory_free)) {}

#define TENSORFLOW_COMPILER_JIT_PJRT_TENSOR_BUFFER_H_

#include <memory>
#include <utility>

#include "xla/pjrt/pjrt_client.h"
#include "tensorflow/core/framework/allocation_description.pb.h"
#include "tensorflow/core/framework/tensor.h"

namespace tensorflow {

// PjRtTensorBuffer is derived from TensorBuffer, which holds a device memory

#include "xla/pjrt/pjrt_client.h"
#include "xla/service/hlo.pb.h"
#include "xla/util.h"
#include "tensorflow/core/framework/device.h"
#include "tensorflow/core/lib/strings/proto_serialization.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/statusor.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {

#include <optional>
#include <string>
#include <vector>

#include "absl/container/flat_hash_set.h"
#include "absl/types/optional.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/util/command_line_flags.h"

namespace tensorflow {

struct XlaAutoJitFlag {
  // Control compilation of operators into XLA computations on CPU and GPU

// 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

#include <string>
#include <utility>

#include "absl/status/statusor.h"
#include "absl/strings/string_view.h"
#include "tensorflow/core/lib/monitoring/counter.h"
#include "tensorflow/core/lib/monitoring/gauge.h"
#include "tensorflow/core/lib/monitoring/sampler.h"
#include "tensorflow/core/protobuf/fingerprint.pb.h"

namespace tensorflow {
namespace metrics {

const char kFingerprintFound[] = "FOUND";

#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/common_runtime/optimization_registry.h"
#include "tensorflow/core/common_runtime/process_function_library_runtime.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/graph/graph.h"

#include <set>
#include <string>
#include <utility>
#include <vector>

#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/protobuf/saved_model.pb.h"

namespace tensorflow {
namespace saved_model {