// CHECK-LABEL: testBatchMatMulToMatMul
func.func @testBatchMatMulToMatMul(%arg0: tensor<2x3xf32>, %arg1: tensor<3x2xf32>) -> tensor<2x2xf32> {
  // CHECK: %0 = "tf.MatMul"(%arg0, %arg1) <{grad_a = false, grad_b = false, transpose_a = false, transpose_b = false}> {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<2x3xf32>, tensor<3x2xf32>) -> tensor<2x2xf32>

}

// -----

func.func @add_with_quantized_i16_broadcasting(tensor<2x2xf32>, tensor<1xf32>) -> tensor<2x2x!quant.any<i16:f32>> {
^bb0(%arg0: tensor<2x2xf32>, %arg1: tensor<1xf32>):
  // expected-error @+1 {{Operands should have valid shapes and element type needs to match}}
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<2x2xf32>, tensor<1xf32>) -> tensor<2x2x!quant.any<i16:f32>>

// -----

func.func @testDynamicStitch(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  %indices = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
  %0 = "tf.DynamicStitch"(%indices, %arg0) : (tensor<2xi32>, tensor<2x2xf32>) -> tensor<2x2xf32>
  func.return %0 : tensor<2x2xf32>
}

// -----

func.func @testDynamicStitch() -> tensor<2x2xf32> {