// CHECK-NEXT: return %[[FINAL]] : tensor<1x97xi32>
  func.return %result : tensor<1x97xi32>
}

// -----

// CHECK-LABEL: strided_slice_nonconstant_begin_end_with_start_end_mask
// CHECK-SAME: (%[[INPUT:.*]]: tensor<32x1x97xi32>, %[[BEGIN:.*]]: tensor<3xi32>, %[[END:.*]]: tensor<3xi32>)

  %0 = "mhlo.gather"(%arg0, %arg1) {
    dimension_numbers = #mhlo.gather<
      collapsed_slice_dims = [1],
      index_vector_dim = 1,
			offset_dims = [0],
			start_index_map = [1],
    >,
    indices_are_sorted = false,
    slice_sizes = dense<1> : tensor<2xi64>
  } : (tensor<1x20xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
  func.return %0 : tensor<1x1xi32>
}

    %1 = "tf.MatMul"(%arg0, %0) {device = "", transpose_a = false, transpose_b = false} : (tensor<1x3xi32>, tensor<3x1xi32>) -> tensor<1x1xi32>
    func.return %1 : tensor<1x1xi32>
  }
  func.func @predict(
    ) -> (tensor<0x!tf_type.string> {tf_saved_model.index_path = ["r"]})
      attributes {

  %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>)...

  func.return %0 : tensor<i32>
}

// -----

// CHECK-LABEL: constant_tensor_vector
func.func @constant_tensor_vector(%arg0: vector<1x2xi32>) -> tensor<1x2xi32> {
  %0 = "tfr.constant_tensor"(%arg0) : (vector<1x2xi32>) -> tensor<1x2xi32>
  func.return %0 : tensor<1x2xi32>
}

// -----

// CHECK-LABEL: constant_tensor_array
func.func @constant_tensor_array() -> !tfr.tensor {

  %3 = mhlo.reshape %2 : (tensor<9xi32>) -> tensor<1x9xi32>
  %4:2 = mhlo.reduce(%arg0 init: %1), (%3 init: %0) across dimensions = [1] : (tensor<1x9xi32>, tensor<1x9xi32>, tensor<i32>, tensor<i32>) -> (tensor<1xi32>, tensor<1xi32>)
    reducer(%arg1: tensor<i32>, %arg3: tensor<i32>) (%arg2: tensor<i32>, %arg4: tensor<i32>)  {
    %6 = mhlo.compare  GE, %arg1, %arg3 : (tensor<i32>, tensor<i32>) -> tensor<i1>

// CHECK:           %[[SUB:.*]] = "tf.Sub"(%[[CST]], %[[SCATTER]]) : (tensor<i32>, tensor<1x24xi32>) -> tensor<1x24xi32>
// CHECK:           %[[MUL:.*]] = "tf.Mul"(%[[SUB]], %[[CAST0]]) : (tensor<1x24xi32>, tensor<1x24xi32>) -> tensor<1x24xi32>

    %8 = stablehlo.convert %7 : (tensor<1x2xi32>) -> tensor<1x2xf32>
    %9 = stablehlo.convert %2 : (tensor<2x3xi8>) -> tensor<2x3xf32>
    %10 = stablehlo.dot_general %8, %9, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    %11 = stablehlo.convert %3 : (tensor<1x3xi32>) -> tensor<1x3xf32>

    %0 = "tf.Const"() {value = dense<1> : tensor<i64>} : () -> tensor<i64>
    %1 = "tf.Const"() {value = dense<0> : tensor<1x1xi32>} : () -> tensor<1x1xi32>
    %2 = "tf.Const"() {value = dense<[7, 7, 3, 64]> : tensor<4xi32>} : () -> tensor<4xi32>
    %3 = "tf.Const"() {value = dense<[[0, 0], [3, 3], [3, 3], [0, 0]]> : tensor<4x2xi32>} : () -> tensor<4x2xi32>

^bb0(%arg0: tensor<1x384xf32>, %arg1: tensor<1x96xf32>, %arg2: tensor<384x480xf32>, %arg3: tensor<384xf32>, %arg4: tensor<1x96xf32>):
  %0:4 = "tfl.basic_lstm"(%arg0, %arg1, %arg2, %arg3, %arg4) {fused_activation_function = "RELU", cell_clip = 1.0 : f32, proj_clip = 2.0 : f32} : (tensor<1x384xf32>, tensor<1x96xf32>, tensor<384x480xf32>, tensor<384xf32>, tensor<1x96xf32>) -> (tensor<1x96xf32>, tensor<1x96xf32>, tensor<1x480xf32>, tensor<1x384xf32>)