},
      R"pbdoc(
      Returns and clears the list of collected errors in ErrorCollector.
    )pbdoc");
  m.def(
      "FlatBufferToMlir",
      [](const std::string& model, bool input_is_filepath) {
        return tflite::FlatBufferFileToMlir(model, input_is_filepath);
      },
      R"pbdoc(
      Returns MLIR dump of the given TFLite model.
    )pbdoc");

        }
        py_function_library.SaveExportedModel(
            dst_saved_model_path, *exported_model, src_saved_model_path, tags,
            signature_def_map);

        return absl::OkStatus();
      },
      R"pbdoc(
      Quantizes a model that went through quantization-aware training (QAT)
      saved at `src_saved_model_path`. The resulting model will be saved to
      `dst_saved_model_path`. Returns an OK sataus when successful, otherwise

                          module_bytecode.status().ToString().c_str());
          throw py::error_already_set();
        }
        return py::bytes(module_bytecode.value());
      },
      R"pbdoc(
        Converts a TensorFlow SavedModel into StableHLO bytecode.

        * input-path: The path to the input TensorFlow SavedModel.
        * exported-model-signatures: Comma-separated list of exported model

			return errSRBackendIssue(err)
		}

		for pname, pdoc := range allPolicyDocs {
			policyJSON, err := json.Marshal(pdoc.Policy)
			if err != nil {
				return wrapSRErr(err)
			}
			err = c.IAMChangeHook(ctx, madmin.SRIAMItem{
				Type:      madmin.SRIAMItemPolicy,
				Name:      pname,
				Policy:    policyJSON,
				UpdatedAt: pdoc.UpdateDate,
			})
			if err != nil {