%w = arith.constant dense<127.0> : tensor<1x1x1x8x16xf32>
  %b = arith.constant dense<0.0> : tensor<16xf32>
  %conv_3d = "tfl.conv_3d"(%arg0, %w, %b) {dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x32x32x32x8xf32>, tensor<1x1x1x8x16xf32>, tensor<16xf32>) -> tensor<1x32x32x32x16xf32>

  // CHECK: %[[VAL2:.+]] = "tfl.dequantize"(%[[VAL1]]) : (tensor<1x1x1x8x16x!quant.uniform<{{.+}}>>) -> tensor<1x1x1x8x16xf32>

func.func @main(tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> {
^bb0(%arg0: tensor<1x6x6x16xf32>):
  // CHECK: "tfl.average_pool_2d"(%{{.*}}) <{filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32}> : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>

  // Unsupported strides
  %2 = "tf.MaxPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [1, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 3]} : (tensor<1x1x1x16xf32>) -> tensor<1x1x1x16xf32>

  %5 = arith.addf %0, %1 : tensor<1x1x1x16xf32>
  %6 = arith.addf %2, %5 : tensor<1x1x1x16xf32>
  func.return %6 : tensor<1x1x1x16xf32>

  func.return %0 : tensor<1x1x1x16xf32>

  func.return %1 : tensor<1x1x1x16xf32>

// CHECK: %[[avgp:.*]] = "tfl.average_pool_2d"(%arg0)
// CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[avgp]]) : (tensor<1x1x1x16x!quant.uniform<u8:f32, 7.812500e-03:128>>) -> tensor<1x1x1x16xf32>
// CHECK: return %[[dq]] : tensor<1x1x1x16xf32>
}

// CHECK-LABEL: QuantizeReshape2D

^bb0(%arg0: tensor<1x7x7x16xf32>):
  // expected-error @+1 {{requires attribute 'padding'}}
  %0 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", ksize = [1, 7, 7, 1], strides = [1, 1, 1, 1]} : (tensor<1x7x7x16xf32>) -> tensor<1x1x1x16xf32>
  func.return %0 : tensor<1x1x1x16xf32>
}

// -----

func.func @testAvgPoolWrongPadding(tensor<1x7x7x16xf32>) -> tensor<1x1x1x16xf32> {

  func.return %1 : tensor<1x1x1x1x1xf32>

  // CHECK-DAG: %[[CST1:.*]] = "tfl.no_value"() <{value}> : () -> none
  // CHECK-DAG: %[[CST2:.*]] = arith.constant dense<2.000000e+00> : tensor<1x1x1x1x1xf32>

    } : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>
  func.return %1 : tensor<1x1x1x16xf32>

// CHECK: %0 = "tfl.dequantize"(%arg0)
// CHECK: %1 = "tfl.average_pool_2d"(%0)
// CHECK: %2 = "tfl.quantize"(%1)
// CHECK: %3 = "tfl.dequantize"(%2)
// CHECK: return %3 : tensor<1x1x1x16xf32>
}

// CHECK-LABEL: QuantizeMaximum

}

// -----

func.func @sub_with_quantized_i8_five_dim_broadcasting(tensor<1x1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x1x1x1x1x!quant.any<i8:f32>> {
^bb0(%arg0: tensor<1x1x1x1x1xf32>, %arg1: tensor<1xf32>):
  %0 = "tfl.sub"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1x1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x1x1x1x1x!quant.any<i8:f32>>
  func.return %0#0 : tensor<1x1x1x1x1x!quant.any<i8:f32>>
}