%2, %3, %3, %3, %3, %3, %3, %3, %5, %5, %4, %4) {_tflite_input_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19], device = ""} : (tensor<28x1x28xf32>, tensor<16x28xf32>, tensor<16x28xf32>, tensor<16x28xf32>, tensor<16x28xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16x16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1x16xf32>, tensor<1x16xf32>)...

  %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.func @testGatherV2(%arg0: tensor<16x2x3xf32>, %arg1: tensor<16x5xi32>) -> tensor<16x2x5x3xf32> {
  %0 = "tf.Const"() { value = dense<[-1]> : tensor<1xi32> } : () -> tensor<1xi32>
  %1 = "tf.GatherV2"(%arg0, %arg1, %0) {batch_dims = -1 : i64} : (tensor<16x2x3xf32>, tensor<16x5xi32>, tensor<1xi32>) -> tensor<16x2x5x3xf32>
  func.return %1 : tensor<16x2x5x3xf32>
}

    func.return %0 : tensor<?x?x?x?x?xf32>
  }

  // CHECK-LABEL: testXlaConvV2OpInvalidKernelTensorShapeDim
  func.func @testXlaConvV2OpInvalidKernelTensorShapeDim(%lhs: tensor<8x4x16x16x16xf32>, %rhs: tensor<16x16xf32>) -> (tensor<?x?x?x?x?xf32> ) {
    %feature_group_count = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    %rhs_dilation = "tf.Const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>