graph = ops.get_default_graph()
    for op in graph.get_operations():
      if op.type == '_Retval':
        expected_node_name = op.name
        if op.get_attr('tf_saved_model.index_path') is not None:
          index_path_name = op.get_attr('tf_saved_model.index_path')[0]
          index_path_name = index_path_name.decode('utf-8').split(':')[0]
          try:

	MOVD R8, ret+0(FP)
	RET

TEXT ·get_GetxattrAddr(SB), NOSPLIT|NOFRAME, $0-8
	MOVD $·Getxattr(SB), R8
	MOVD R8, ret+0(FP)
	RET

TEXT ·get_NanosleepAddr(SB), NOSPLIT|NOFRAME, $0-8
	MOVD $·Nanosleep(SB), R8
	MOVD R8, ret+0(FP)
	RET

TEXT ·get_SetxattrAddr(SB), NOSPLIT|NOFRAME, $0-8
	MOVD $·Setxattr(SB), R8
	MOVD R8, ret+0(FP)
	RET

TEXT ·get_Wait4Addr(SB), NOSPLIT|NOFRAME, $0-8
	MOVD $·Wait4(SB), R8

    flexbuffers::Builder* builder) {
  auto int_attr = mlir::dyn_cast_or_null<IntegerAttr>(attrs.get(attribute));
  if (!int_attr) {
    return func.emitError()
           << attribute.c_str() << " attribute is not set or not an integer";
  }
  builder->Int(attribute.c_str(), int_attr.getInt());
  return success();
}

LogicalResult ConvertSSDPostProcessFunc::AddFloatAttr(

  static bool AllowDynamicRangeQuantizedOperand(
      Operation* quantized_op, const CustomMap& custom_op_map) {
    auto call_op = cast<TF::PartitionedCallOp>(quantized_op);
    StringRef function_name =
        call_op.getFAttr().cast<FlatSymbolRefAttr>().getValue();
    // The below can be generalized as there are more read-only ops added such
    // as slice.
    const bool is_gather = function_name.contains("gather");

    float max_percentile;
    OP_REQUIRES_OK(context, context->GetAttr("calibration_method",
                                             &calibration_method_value));
    OP_REQUIRES_OK(context, context->GetAttr("num_bins", &num_bins));
    OP_REQUIRES_OK(context,
                   context->GetAttr("min_percentile", &min_percentile));
    OP_REQUIRES_OK(context,
                   context->GetAttr("max_percentile", &max_percentile));

    .SetIsStateful()
    .SetShapeFn([](shape_inference::InferenceContext* c) {
      c->set_output(0, c->Scalar());
      DataType t;
      TF_RETURN_IF_ERROR(c->GetAttr("dtype", &t));
      PartialTensorShape p;
      TF_RETURN_IF_ERROR(c->GetAttr("shape", &p));
      shape_inference::ShapeHandle s;
      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(p, &s));
      c->set_output_handle_shapes_and_types(

    string file_name;
    string func_name;
    string node_name;
    OP_REQUIRES_OK(ctx, ctx->GetAttr("log_dir_path", &log_dir_path));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("enabled", &enabled_));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("file_name", &file_name));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("func_name", &func_name));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("node_name", &node_name));
    OP_REQUIRES_OK(ctx, ctx->env()->RecursivelyCreateDir(log_dir_path));

            isinstance(field_info, params.Param)
            and getattr(field_info, "in_", None) is None
        ):
            field_info.in_ = params.ParamTypes.query
        use_annotation_from_field_info = get_annotation_from_field_info(
            use_annotation,
            field_info,
            param_name,
        )
        if not field_info.alias and getattr(field_info, "convert_underscores", None):

      return false;
    }
    // FusedMatMul kernel does not support grad_a/grad_b attrs
    if ((matmul->hasAttr("grad_a") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_a")).getValue()) ||
        (matmul->hasAttr("grad_b") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_b")).getValue())) {
      (void)rewriter.notifyMatchFailure(matmul, [&](Diagnostic &diag) {
        diag << "FusedMatMul kernel does not support grad_a/grad_b attrs";

                                          dq.getArg());
      dq.getResult().replaceAllUsesWith(dcast);
      if (auto extra_attr = op->getAttr(mlir::quant::kVolatileOpAttrName)) {
        dcast->setAttr(mlir::quant::kVolatileOpAttrName, extra_attr);
      }
      dq.erase();
    } else if (auto q = llvm::dyn_cast<quantfork::QuantizeCastOp>(op)) {
      auto out_type = q.getResult().getType();