func.return %2 : tensor<1x32x32x16x!quant.uniform<i8:f32, 1.0>>
}

// -----

// CHECK-LABEL: @test_conv2d_replace_float
func.func @test_conv2d_replace_float(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x16xf32> {

  // CHECK: %[[VAL_1:.*]] = "tf.Identity"(%[[VAL_0]]) : (tensor<*xf32>) -> tensor<*xf32>
  // CHECK: return %[[VAL_1]]
  %0 = "tf.Conv2D"(%arg2, %arg1) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true} : (tensor<8x32x32x3xf32>, tensor<1x1x3x128xf32>) -> tensor<*xf32>

// CHECK:  %1 = "tfl.transpose"(%arg0, %cst) : (tensor<4x8x32x32xf32>, tensor<4xi32>) -> tensor<4x32x32x8xf32>
// CHECK:  %cst_0 = arith.constant dense<64> : tensor<2xi32>
// CHECK:  %2 = "tfl.resize_nearest_neighbor"(%1, %cst_0) <{align_corners = false, half_pixel_centers = true}> : (tensor<4x32x32x8xf32>, tensor<2xi32>) -> tensor<4x64x64x8xf32>
// CHECK:  %cst_1 = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>

// 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>

      tf_executor.fetch %outputs_2 : tensor<*xi1>
    }
    return %0 : tensor<*xi1>
  }
  func.func private @image_sample_write_summary_summary_cond_false_710(%arg0: tensor<3x32x32x3xf32>, %arg1: tensor<*x!tf_type.resource>, %arg2: tensor<i64>) -> tensor<*xi1> attributes {tf._construction_context = "kEagerRuntime"} {
    %0 = tf_executor.graph {