func.return %8#2 : tensor<28x1x8xf32>...

    : ( tensor<1x28x28xf32>,
        tensor<20x28xf32>, tensor<20x28xf32>, tensor<20x28xf32>, tensor<20x28xf32>,
        tensor<20x20xf32>, tensor<20x20xf32>, tensor<20x20xf32>, tensor<20x20xf32>,
        none, none, none,
        tensor<20xf32>, tensor<20xf32>, tensor<20xf32>, tensor<20xf32>,
        none, none,
        tensor<1x20xf32>, tensor<1x20xf32>,
        none, none, none, none) -> tensor<1x28x20xf32>

func.func @reshape_vector_shape(tensor<4x4x4xf32>) -> tensor<16x4xf32> {
^bb0(%arg0: tensor<4x4x4xf32>) :
  %shape0 = arith.constant dense<[[16, 4]]> : tensor<1x2xi32>
  // expected-error @+1 {{'tfl.reshape' op requires 'shape' to be rank 1, but got 2}}
  %1 = "tfl.reshape"(%arg0, %shape0) : (tensor<4x4x4xf32>, tensor<1x2xi32>) -> tensor<16x4xf32>
  func.return %1 : tensor<16x4xf32>
}

// -----

  %0 = "tfl.fully_connected" (%arg0, %arg1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x256xf32>, tensor<128x256xf32>, tensor<128xf32>) -> tensor<1x128xf32>
  %1 = "tfl.relu"(%0) : (tensor<1x128xf32>) -> tensor<1x128xf32>
  func.return %1 : tensor<1x128xf32>

  func.return %1 : tensor<16x128xf32>
  // CHECK: return %0 : tensor<16x128xf32>
}

// CHECK-LABEL: FuseTransposeFCLhsToBatchMatmul

// CHECK:           [[VAL_12:%.*]]:4 = "tf.SplitV"([[VAL_7]], [[VAL_10]], [[VAL_11]]) : (tensor<40x8xf32>, tensor<4xi32>, tensor<i32>) -> (tensor<10x8xf32>, tensor<10x8xf32>, tensor<10x8xf32>, tensor<10x8xf32>)
// CHECK-DAG:       [[VAL_13:%.*]] = "tf.Const"() <{value = dense<10> : tensor<4xi32>}> : () -> tensor<4xi32>

  %split_dim_2 = arith.constant dense<1> : tensor<1xi32>
  %4, %5 = "tfl.split"(%split_dim_2, %arg0) {num_splits = 2 : i32} : (tensor<1xi32>, tensor<16x4xf32>) -> (tensor<16x2xf32>, tensor<16x2xf32>)
  %6:2 = "tfl.split"(%split_dim_2, %arg0) {num_splits = 2 : i32} : (tensor<1xi32>, tensor<16x4xf32>) -> (tensor<16x2xf32>, tensor<16x?xf32>)

    return %1 : tensor<1x3xf32>
  }

  func.func private @composite_dot_general_fn(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module} {
    %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    return %0 : tensor<1x3xf32>

func.func @tfl_wrapped_jax_random_uniform(%arg0: tensor<2xui32>) -> tuple<tensor<1x2xf32>> {
  // This is a fake jax random uniform body.
  %0 = stablehlo.constant dense<0.0> : tensor<2xf32>
  %1 = "stablehlo.reshape"(%0) : (tensor<2xf32>) -> tensor<1x2xf32>
  %2 = "stablehlo.tuple"(%1) : (tensor<1x2xf32>) -> tuple<tensor<1x2xf32>>
  func.return %2 : tuple<tensor<1x2xf32>>

  // CHECK: "tfl.conv_2d"(%arg0, %arg1, %arg2) <{dilation_h_factor = 2 : i32, dilation_w_factor = 2 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<1x112x80x128xf32>, tensor<128x3x3x128xf32>, tensor<128xf32>) -> tensor<1x108x76x128xf32>