%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>)

    func.return %1 : tensor<2xf32>
  }

  // CHECK-LABEL: binary_op_broadcast
  func.func @binary_op_broadcast(%arg0: tensor<4x1xf32>, %arg1: tensor<4x1x4xf32>) -> tensor<4x4x4xf32> {
    // CHECK: %[[BROADCAST0:.*]] = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>}> : (tensor<4x1xf32>) -> tensor<4x4x1xf32>

//   %cst = arith.constant dense<1.0> : tensor<16x16x4xf32>
//   %0 = "tfl.conv_2d"...
//   %1 = "tfl.add"(%0, %cst) : (tensor<16x16x4xf32>, tensor<16x16x4xf32>)
// After this optimization:
//   %cst = arith.constant dense<1.0> : tensor<f32>
//   %0 = "tfl.conv_2d"...
//   %1 = "tfl.add"(%0, %cst) : (tensor<16x16x4xf32>, tensor<f32>)

// CHECK-NEXT:    %[[BMM_0:.*]] = "tfl.batch_matmul"(%[[RESHAPED_0]], %[[RESHAPED_1]]) <{adj_x = false, adj_y = false, asymmetric_quantize_inputs = false}> : (tensor<3x5x12xf32>, tensor<3x12x4xf32>) -> tensor<3x5x4xf32>
// CHECK-NEXT:    %[[RESHAPED_BMM:.*]] = mhlo.reshape %[[BMM_0]]
// CHECK-NEXT:    return %[[RESHAPED_BMM]] : tensor<3x5x1x4xf32>
}

    %elem_shape = "tf.Const"() {value = dense<[-1, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
    %size = "tf.Const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>
    %tensors = "tf._SomeOp"() : () -> tensor<3x16x1xf32>
    %indices = "tf.Const"() {value = dense<[2, 5, 9]> : tensor<3xi32>} : () -> tensor<3xi32>
    // CHECK: TensorListReserve
    // CHECK-SAME: tensor<!tf_type.variant<tensor<16x1xf32>>>

// CHECK:           %[[VAL_9:.*]] = "tf.Reshape"(%[[VAL_5]], %[[VAL_8]]) : (tensor<3x2x6x4xf32>, tensor<3xi64>) -> tensor<3x12x4xf32>
// CHECK:           %[[VAL_10:.*]] = "tf.BatchMatMulV3"(%[[VAL_7]], %[[VAL_9]]) <{adj_x = false, adj_y = false, grad_x = false, grad_y = false}> : (tensor<3x5x12xf32>, tensor<3x12x4xf32>) -> tensor<3x5x4xf32>
// CHECK:           %[[VAL_11:.*]] = arith.constant dense<[3, 5, 1, 4]> : tensor<4xi64>