tflite::TensorType ConvertTypeToTensorType(mlir::Type type) {
  if (type.isF16()) {
    return tflite::TensorType_FLOAT16;
  } else if (type.isBF16()) {
    return tflite::TensorType_BFLOAT16;
  } else if (type.isF32()) {
    return tflite::TensorType_FLOAT32;
  } else if (type.isF64()) {
    return tflite::TensorType_FLOAT64;
  } else if (mlir::isa<mlir::TF::StringType>(type)) {
    return tflite::TensorType_STRING;

  typedef internal::Types$i<$for j, [[T$j]]> type;
};

]]

namespace internal {

# define GTEST_TEMPLATE_ template <typename T> class

// The template "selector" struct TemplateSel<Tmpl> is used to
// represent Tmpl, which must be a class template with one type
// parameter, as a type.  TemplateSel<Tmpl>::Bind<T>::type is defined
// as the type Tmpl<T>.  This allows us to actually instantiate the

    %3 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    "tf.AssignAddVariableOp"(%0, %2) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2x8xi32>>>, tensor<i32>) -> ()
    "tf.AssignAddVariableOp"(%1, %3) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2xi32>>>, tensor<i32>) -> ()
    func.return
  }
}

// -----

    %2:2 = tf_executor.island wraps "tf.Identity"(%arg1) {device = "", dtype = "tfdtype$DT_INT32"} : (tensor<10xi32>) -> tensor<10xi32> loc ("bar")
    // The following node would be renamed to bar2
    %3:2 = tf_executor.island wraps "tf.Identity"(%2) {device = "", dtype = "tfdtype$DT_INT32"} : (tensor<10xi32>) -> tensor<10xi32> loc ("bar")

    value {
      list {
        type: DT_FLOAT
        type: DT_FLOAT
      }
    }
  }
  attr {
    key: "Toutputs"
    value {
      list {
        type: DT_FLOAT
      }
    }
  }
  attr {
    key: "mlir_module"
    value {

                addAll(collectImportsForDiagnosticParameter(diagnosticParameter))
            }
        }
    }

    protected fun HLDiagnosticList.containsClashingBySimpleNameType(type: KType): Boolean {
        return diagnostics.any { it.className == type.simpleName }
    }

    protected abstract fun collectImportsForDiagnosticParameter(diagnosticParameter: HLDiagnosticParameter): Collection<String>

            fun visit(type: KClass<*>) {
                if (add(type)) {
                    typeDiscovery.getClassesToVisitFrom(type).forEach(::visit)
                }
            }
            types.forEach(::visit)
        }

        return PreIndex().apply {
            allTypesToVisit.forEach { type ->
                addType(type)

  Status status;
  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;
    tensorflow::TensorShape shape;
    status = v2_reader_->LookupDtypeAndShape(name, &dtype, &shape);
    if (status.ok()) {
      out_tensor->reset(new Tensor(dtype, shape));
      status = v2_reader_->Lookup(name, out_tensor->get());
      if (!status.ok()) out_tensor->reset();
    }
  }

  *graph.add_node() = MakeNode("a", "Placeholder", {},
                               {{"dtype", DT_FLOAT}, {"shape", input_shape}});
  *graph.add_node() = MakeNode("b", "Placeholder", {},
                               {{"dtype", DT_FLOAT}, {"shape", input_shape}});
  *graph.add_node() = MakeNode("c", "Placeholder", {},
                               {{"dtype", DT_FLOAT}, {"shape", input_shape}});

  TF_RETURN_IF_ERROR(op_ptr->SetAttrString("shared_name", shared_name, strlen(shared_name)));
  TF_RETURN_IF_ERROR(op_ptr->SetAttrString("debug_name", debug_name, strlen(debug_name)));
  TF_RETURN_IF_ERROR(op_ptr->SetAttrType("dtype", dtype));
  TF_RETURN_IF_ERROR(op_ptr->SetAttrShape("shape", shape));
  TF_RETURN_IF_ERROR(op_ptr->SetAttrStringList("allowed_devices", allowed_devices));
  int num_retvals = 1;