// to resume the conversion after injecting more information in the middle of
// it.
void AddPostVariableFreezingTFToTFLConversionPasses(
    llvm::StringRef saved_model_dir, const toco::TocoFlags& toco_flags,
    const mlir::TFL::PassConfig& pass_config,
    mlir::OpPassManager* pass_manager) {
  // Note:
  // We need to fuse composite ops before LowerStaticTensorList pass.

  pass_config.enable_hlo_to_tf_conversion = enable_hlo_to_tf_conversion;
  pass_config.disable_hlo_to_tfl_conversion = disable_hlo_to_tfl_conversion;
  pass_config.reduce_type_precision = reduce_type_precision;

  toco::TocoFlags toco_flags;
  toco_flags.set_force_select_tf_ops(!emit_builtin_tflite_ops);
  toco_flags.set_enable_select_tf_ops(emit_select_tf_ops);
  toco_flags.set_allow_custom_ops(emit_custom_ops);

      // quantizer. This code is temporary until 16x8 is fully supported in MLIR
      // quantizer.
      if (act_type == InputDataType::INT16) {
        if (is_toco) {
          // Conditions when using TOCO.
          if (absl::StrContains(op_name, "LSTMOp")) continue;
        } else {
          // Conditions when using MLIR.
          if (!(absl::StrContains(op_name, "LSTMOp") ||

        "//tensorflow/lite/kernels:builtin_ops",
        "//tensorflow/lite/kernels:reference_ops",
        "//tensorflow/lite/schema:schema_fbs",
        "//tensorflow/lite/toco/logging:conversion_log_util",
        "//tensorflow/lite/toco/logging:toco_conversion_log_proto_cc",
        "//tensorflow/lite/toco:model_flags_proto_cc",
        "//tensorflow/lite/toco:toco_convert",
        "//tensorflow/lite/toco:toco_flags_proto_cc",

  // required for post-training quantization.
  const bool infer_tensor_range_;

  // Calculate scales in float instead of double, so that the scales and
  // quantized values are exactly the same with the TOCO quantizer.
  const bool legacy_float_scale_;

  // If true, the model is a floating point graph with QDQ ops to be eliminated
  // and fused into quantized kernels.
  const bool is_qdq_conversion_;
};

#include "tensorflow/lite/graph_info.h"
#include "tensorflow/lite/python/metrics/converter_error_data.pb.h"
#include "tensorflow/lite/schema/schema_conversion_utils.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"
#include "tensorflow/lite/tools/versioning/gpu_compatibility.h"
#include "tensorflow/lite/tools/versioning/op_version.h"
#include "tensorflow/lite/tools/versioning/runtime_version.h"

      return failure();
    }

    // TensorFlow convolution op only has two inputs, while the TFLite one has
    // three, with the bias vector marked as optional. However, TOCO has a
    // dedicated pass, EnsureBiasVectors, to create default bias vectors for all
    // those missing. So we model TFLite convolution op as requiring three

    for the
    [TFLite Optimizing Converter](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/toco/README.md)
    has been expanded, and the command line interface (AKA: `toco`,
    `tflite_convert`) is once again included in the standard `pip` installation.
*   Improved data-loading and text processing with: