%0:4 = "tfl.basic_lstm"(%arg0, %arg1, %arg2, %arg3, %arg4) {fused_activation_function = "TANH", kernel_type = #tfl<lstm_kernel_type_attr BASIC>} : (tensor<1x384xf32>, tensor<1x96xf32>, tensor<384x480xf32>, tensor<384xf32>, tensor<1x96xf32>) -> (tensor<1x96xf32>, tensor<1x96xf32>, tensor<1x480xf32>, tensor<1x384xf32>)...

  %cst = arith.constant dense<3.0> : tensor<40xf32>
  %cst2 = arith.constant dense<2.0> : tensor<40xf32>

  %0 = "tfl.fully_connected" (%arg0, %arg1, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<40x37xf32>, tensor<40x37xf32>, tensor<40xf32>) -> (tensor<40x40xf32>)
  %1 = "tfl.add"(%0, %cst2) {fused_activation_function = "NONE"} : (tensor<40x40xf32>, tensor<40xf32>) -> tensor<40x40xf32>

  %0 = "tfl.pseudo_const"() {value = dense<0.0> : tensor<40xf32>} : () -> tensor<40xf32>
  %1 = "tfl.fully_connected"(%arg0, %arg1, %0) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x37xf32>, tensor<40x37xf32>, tensor<40xf32>) -> tensor<1x40xf32>
// CHECK: "tfl.fully_connected"

func.func @DoNotConvertConv2DWhenFilterTypeDimIsNotDecided(%arg0 : tensor<?x?x?x96xf32>, %arg1 : tensor<3x3x96x?xf32>) -> tensor<?x?x?x?xf32> {
  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true} : (tensor<?x?x?x96xf32>, tensor<3x3x96x?xf32>) -> tensor<?x?x?x?xf32>
  func.return %0 : tensor<?x?x?x?xf32>

//                (tensor<4x2xf32>, tensor<4x2xf32>, tensor<4x2xf32>)
//
// will be converted into:
//
//   %0 = "mhlo.slice"(%input) {
//             limit_indices = dense<[4, 2]> : tensor<2xi64>,
//             start_indices = dense<0> : tensor<2xi64>,
//             strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x2xf32>