}

func.func @squeezeSingleAxis(%arg0: tensor<2x1x2xf32>) -> tensor<2x2xf32> {
  %0 = "tf.Squeeze"(%arg0) {squeeze_dims = [1]} : (tensor<2x1x2xf32>) -> tensor<2x2xf32>
  func.return %0 : tensor<2x2xf32>

// CHECK-LABEL:squeezeSingleAxis
// CHECK:  "tfl.squeeze"(%arg0) <{squeeze_dims = [1]}> : (tensor<2x1x2xf32>) -> tensor<2x2xf32>
}

// -----

func.func @testBatchMatMulV2InvalidBroadcastingBatchDimensionWithHigherYRank(%lhs: tensor<2x5x10xf32>, %rhs: tensor<10x10x10x10xf32>) {
  // expected-error @+1 {{found incompatible broadcast batch dimensions for lhs shape 'tensor<2x5x10xf32>' and rhs shape 'tensor<10x10x10x10xf32>'}}
  %0 = "tf.BatchMatMulV2"(%lhs, %rhs) : (tensor<2x5x10xf32>, tensor<10x10x10x10xf32>) -> tensor<10x10xf32>
}

// -----

func.func @testFullyConnectedWithBadInputShape(%arg0: tensor<2x2x11xf32>, %arg1: tensor<40x40xf32>, %arg2: none) -> tensor<40xf32> {
  // expected-error @+1 {{expect 'input' num_elements % 40 == 0, got input type 'tensor<2x2x11xf32>'}}
  %0 = "tfl.fully_connected"(%arg0, %arg1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<2x2x11xf32>, tensor<40x40xf32>, none) -> tensor<1x40xf32>