return %1 : tensor<4x8x64x64xf32>
}
func.func private @XlaCallModule_tfl.resize_nearest_neighbor.impl_1(%arg0: tensor<4x8x32x32xf32>) -> tensor<4x8x64x64xf32> {
  %0 = call @XlaCallModule__resize_1(%arg0) : (tensor<4x8x32x32xf32>) -> tensor<4x8x64x64xf32>
  return %0 : tensor<4x8x64x64xf32>
}

  // Check that Conv2D computed in NCHW format, and all redundant transpose
  // operations removed from the function.

  // CHECK: %[[CONV:[0-9]*]] = "tf.Conv2D"(%arg0, %arg1)
  // CHECK-SAME: data_format = "NCHW"
  // CHECK-SAME: -> tensor<1x8x32x32xf32>

  // CHECK: return %[[CONV]]

  func.return %4 : tensor<1x8x32x32xf32>

  // CHECK-SAME: dilations = [1, 4, 2, 3]
  // CHECK-SAME: explicit_paddings = [1, 2, 7, 8, 3, 4, 5, 6]
  // CHECK-SAME: padding = "EXPLICIT"
  // CHECK-SAME: strides = [5, 8, 6, 7]
  // CHECK-SAME: (tensor<1x3x32x32xf32>, tensor<4xi32>, tensor<1x8x32x32xf32>)
  // CHECK-SAME: -> tensor<1x1x3x8xf32>

  // CHECK: return %[[CONV2D_BACKPROP]]

  %0 = "tf.Conv2DBackpropFilter"(%input, %filter_sizes, %out_backprop)
       {
         data_format = "NHWC",

// dilations, etc...). This test only verifies that changing convolution data
// layout will update all the attributes.

// CHECK-LABEL: func @transposeConv2D
func.func @transposeConv2D(%input: tensor<1x3x32x32xf32>, %filter: tensor<1x1x3x8xf32>) -> tensor<1x8x7x6xf32> {

  // CHECK: %[[ARG_PERM:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]])