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(

    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));

      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();

namespace mlir {
namespace odml {

// Ensure an attribute named attr_name exists and it is of type AttrType.
// If so, sets the `out_attr` pointer to point to the casted attribute.
template <typename AttrType>
bool EnsureAttribute(const DictionaryAttr& composite_attributes,
                     const std::string& attr_name, AttrType* out_attr) {
  Attribute attr = composite_attributes.get(attr_name);
  if (!mlir::isa_and_nonnull<AttrType>(attr)) {

    OP_REQUIRES_OK(context,
                   context->GetAttr("output_file_path", &output_file_path));
    OP_REQUIRES_OK(context, context->env()->NewWritableFile(output_file_path,
                                                            &output_file_));

    OP_REQUIRES_OK(context, context->GetAttr("ids", &ids_));
    OP_REQUIRES_OK(context, context->GetAttr("calibration_methods",

        float_attr.getSplatValue<APFloat>().isExactlyValue(raw_value))
      return true;
  } else if (mlir::isa<IntegerType>(element_type)) {
    DenseIntElementsAttr int_attr;
    if (matchPattern(value, m_Constant(&int_attr)) && int_attr.isSplat() &&
        int_attr.getSplatValue<APInt>() == raw_value)
      return true;
  }

  return false;
}

// Returns true if `op` is placed on GPU device, and false if it's on other

  // Check that there are no duplicated exported_names.
  DenseMap<StringRef, Operation *> exported_name_to_op;
  for (auto &op : module) {
    auto attr = op.getAttr(exported_names_ident);
    if (!attr) continue;
    // If this verifier is called before we verify the
    // 'tf_saved_model.exported_names' attribute, then it might be invalid.