// CHECK:         }
func.func @broadcast_add(%arg0: tensor<1x1xf32>, %arg1: tensor<1x1000xf32>) -> (tensor<1x1000xf32>, tensor<1x1000xf32>) {
  %0 = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>}> : (tensor<1x1xf32>) -> tensor<1x1000xf32>
  %1 = mhlo.add %0, %arg1 : tensor<1x1000xf32>
  %2 = mhlo.add %arg1, %0 : tensor<1x1000xf32>

  func.return %0 : tensor<2x2xf16>
}

// -----

// CHECK-LABEL: squeeze
func.func @squeeze(%arg0: tensor<1x1x10xf32>) -> tensor<1x10xf32> {
  // CHECK: mhlo.reshape
  %0 = "tf.Squeeze"(%arg0) : (tensor<1x1x10xf32>) -> tensor<1x10xf32>
  func.return %0 : tensor<1x10xf32>
}

// -----

// CHECK-LABEL: squeeze_ranked
func.func @squeeze_ranked(%arg0: tensor<?x?x?xf32>) -> tensor<?xf32> {

  %5 = "tfl.log"(%4) : (tensor<10x1x10xf32>) -> tensor<10x1x10xf32>
  %6 = tfl.sub(%2, %5) {fused_activation_function = "NONE"} : (tensor<10x10x10xf32>, tensor<10x1x10xf32>) -> tensor<10x10x10xf32>
  return %6 : tensor<10x10x10xf32>
 // CHECK-NOT: "tfl.log_softmax"(%arg0) : (tensor<10x10x10f32>) -> tensor<10x10x10xf32>
}