"total" : 5,
    "successful" : 5,
    "failed" : 0
  },
  "hits" : {
    "total" : 1,
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "minio_events",
        "_type" : "event",
        "_id" : "images/myphoto.jpg",
        "_score" : 1.0,
        "_source" : {
          "Records" : [
            {
              "eventVersion" : "2.0",

type rtype struct {
	t abi.Type
}

func (t *rtype) common() *abi.Type {
	return &t.t
}

func (t *rtype) uncommon() *abi.UncommonType {
	return t.t.Uncommon()
}

type aNameOff = abi.NameOff
type aTypeOff = abi.TypeOff
type aTextOff = abi.TextOff

// ChanDir represents a channel type's direction.
type ChanDir int

const (
	RecvDir ChanDir             = 1 << iota // <-chan

}

std::string ExpectedString(const char* type) {
  const auto format_str = R"str(kernel {
  op: "TypeOp%s"
  device_type: "FakeDeviceName1"
  constraint {
    name: "T"
    allowed_values {
      list {
        type: %s
      }
    }
  }
}
)str";
  return absl::StrFormat(format_str, type, type);
}

#define TEST_KERNEL_TYPE_CONSTRAINT(tf_type, dtype)                          \

  %ta:2 = "tf.TensorArrayV3"(%size) {dtype = f32, element_shape = #tf_type.shape<3>, dynamic_size = false, clear_after_read = true, identical_element_shapes = true, tensor_array_name = "ta"} : (tensor<i32>) -> (tensor<!tf_type.resource>, tensor<f32>)

      )

    return inputs, outputs

  def _create_data_generator(
      self,
      input_key: str,
      shape: Sequence[int],
      minval: float = -1.0,
      maxval: float = 1.0,
      dtype: dtypes.DType = dtypes.float32,
      num_examples: int = 8,
  ) -> repr_dataset.RepresentativeDataset:
    """Creates a data generator to be used as representative dataset.

  %1 = "tf_device.cluster"() ({
    %2 = "tf.ReadVariableOp"(%0) {dtype = i32} : (tensor<*x!tf_type.resource<tensor<*xi32>>>) -> tensor<1xi32>
    %3 = "tf.SomeComputation"(%2) : (tensor<1xi32>) -> (tensor<*xi32>)
    "tf.AssignVariableOp"(%0, %3) {dtype = i32} : (tensor<*x!tf_type.resource<tensor<*xi32>>>, tensor<*xi32>) -> ()
    %4 = "tf.ReadVariableOp"(%0) {dtype = i32} : (tensor<*x!tf_type.resource<tensor<*xi32>>>) -> tensor<1xi32>

    if attr_val.HasField('s'):
      return '"{}"'.format(attr_val.s.decode())
    # type
    if attr_val.HasField('type'):
      if attr_val.type == types_pb2.DT_FLOAT:
        return 'f32'
      elif attr_val.type == types_pb2.DT_INT32:
        return 'i32'
      elif attr_val.type == types_pb2.DT_INT64:
        return 'i64'
      elif attr_val.type == types_pb2.DT_BOOL:
        return 'i1'
    # list

                                         .WithAttr("dtype", DT_FLOAT)
                                         .WithAttr("value", Tensor()));
    Node* b = ops::SourceOp("Const", builder.opts()
                                         .WithName("b")
                                         .WithAttr("dtype", DT_FLOAT)
                                         .WithAttr("value", Tensor()));

        absl::StrCat(src_node->name(), "_", src_slot, "_retval"), kRetValOp,
        NodeDebugInfo(src_node->def()));
    DataType dtype = src_node->output_type(src_slot);
    builder.Attr("T", dtype);
    builder.Attr("index", ret_index);
    builder.Input(src_image->name(), src_slot, dtype);
    Status s = builder.Finalize(&ret_def);
    if (!s.ok()) return s;
    TF_ASSIGN_OR_RETURN(Node * ret, graph_->AddNode(ret_def));

            name_and_attrs.attr().find("dtype")->second.type());
  TF_DeleteBuffer(serialized_attr_values);

  TFE_Op* var_op_2 = TFE_NewOp(ctx, "VarHandleOp", status);

  string serialized_dtype;
  ASSERT_TRUE(name_and_attrs.attr().find("dtype")->second.SerializeToString(
      &serialized_dtype));
  TFE_OpSetAttrValueProto(
      var_op_2, "dtype",