%tconv_s = "quantfork.stats"(%tconv) {layerStats = dense<[0.000000e+00, 1.000000e+01]> : tensor<2xf32>} : (tensor<1x32x42x128xf32>) -> tensor<1x32x42x128xf32>
  func.return %tconv_s : tensor<1x32x42x128xf32>

// CHECK-DAG: %[[b:.*]] = arith.constant dense<0.000000e+00> : tensor<1x32x42x128xf32>

       } : (tensor<1x32x32x3xf32>, tensor<4xi32>, tensor<1x32x32x8xf32>)
         -> tensor<1x1x3x8xf32>

  func.return %0 : tensor<1x1x3x8xf32>
}

// CHECK-LABEL: func @transposeConv2DBackpropInput
func.func @transposeConv2DBackpropInput(
  %input_sizes: tensor<4xi32>,
  %filter: tensor<1x1x3x8xf32>,
  %out_backprop: tensor<1x32x32x8xf32>
) -> tensor<1x32x32x3xf32> {

// RUN: tf-opt %s -canonicalize | FileCheck %s

// CHECK-LABEL: func @testShape
func.func @testShape(tensor<f32>, tensor<1x32x32x16xf32>, tensor<*xf32>) -> (tensor<0xi32>, tensor<?xi32>, tensor<?xi32>) {
^bb0(%arg0: tensor<f32>, %arg1: tensor<1x32x32x16xf32>, %arg2: tensor<*xf32>):

  // CHECK-DAG: tf.Const{{.*}} dense<> : tensor<0xi32>
  %0 = "tf.Shape"(%arg0) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<f32>) -> tensor<0xi32>

}

// -----

func.func @testBiasAddNoDataFormatOk(tensor<1x32x32x16xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32> {
^bb0(%arg0: tensor<1x32x32x16xf32>, %arg1: tensor<16xf32>):
  %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT"}: (tensor<1x32x32x16xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
  func.return %0 : tensor<1x32x32x16xf32>
}

// -----

  %cst = "tfl.no_value"() {value = unit} : () -> none
  // CHECK: _arithmetic_count = 176160768 : i64
  %0 = "tfl.transpose_conv"(%arg0, %arg1, %arg2, %cst) {padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32, fused_activation_function = "NONE"} : (tensor<4xi32>, tensor<32x4x4x128xf32>, tensor<1x32x42x128xf32>, none) -> tensor<1x64x84x32xf32>

  // CHECK: %0 = "tfl.conv_2d"(%arg0, %arg1, %cst)
}

// CHECK-LABEL: fuseAddIntoTransposeConv
func.func @fuseAddIntoTransposeConv(%arg0: tensor<1x32x42x128xf32>) -> tensor<1x64x84x32xf32> {
  %cst = arith.constant dense<1.5> : tensor<32xf32>
  %cst_0 = arith.constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]> : tensor<16xf32>