inferredReturnTypes.assign({result_type});
    return success();
  }

  auto stride_h = op.getStrideHAttr().getInt();
  auto stride_w = op.getStrideWAttr().getInt();
  auto dilation_h = op.getDilationHFactorAttr().getInt();
  auto dilation_w = op.getDilationWFactorAttr().getInt();

  // We don't have EXPLICIT PADDING in TfLite.
  auto paddings = op.getPadding();
  tensorflow::Padding padding;

    // Constructs dilation array.
    SmallVector<int64_t, 4> dilation;
    if (auto rhs_dilation = conv_op.getRhsDilation()) {
      // For example, [2, 3] -> [1, 2, 3, 1].
      dilation.emplace_back(1);
      dilation.append(rhs_dilation.value().getValues<int64_t>().begin(),
                      rhs_dilation.value().getValues<int64_t>().end());
      dilation.emplace_back(1);
    } else {

      },
      {
          'testcase_name': 'dilation_bn',
          'activation_fn': None,
          'has_bias': False,
          'has_batch_norm': True,
          'target_opset': quant_opts_pb2.TF,
          'input_shape_dynamic': False,
          'enable_per_channel_quantization': False,
          'dilations': [1, 2, 2, 1],
      },
      {
          'testcase_name': 'with_bias',

  int64_t dilations_size = dilations.size();
  if (dilations_size != num_dims)
    return emitOptionalError(
        location, "requires dilations attribute length to be ", num_dims);
  if (llvm::any_of(dilations, is_not_positive))
    return emitOptionalError(location, "requires positive dilations");

  return success();
}

// Verifies that,

    // Only 2d pooling is supported in TFLite.
    if (op.getWindowDimensions().size() != 4) {
      return failure();
    }

    // reduce_window op with dilations or padding will supported later.
    // TODO: b/321099943 - Support reduce_window op with dilations and padding.
    if (op.getBaseDilations().has_value() ||
        op.getWindowDilations().has_value() || op.getPadding().has_value()) {
      return failure();
    }

  %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>
  %1 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="VALID", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>

        verify_size(window_strides.size(), "window strides") &&
        verify_size(padding.size(), "padding entries") &&
        verify_size(lhs_dilation.size(), "lhs dilation factors") &&
        verify_size(rhs_dilation.size(), "rhs dilation factors")))
    return std::nullopt;

  xla::Window window;
  for (size_t i = 0; i < window_dimensions.size(); i++) {
    auto dim = window.add_dimensions();

}

// -----

func.func @testConv2D(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>) -> tensor<256x30x30x16xf32> {
  // expected-error @+1 {{requires dilations attribute length to be 4}}
  %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "SAME", strides = [1, 1, 1, 1], dilations = [1, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x30x30x16xf32>
  func.return %0 : tensor<256x30x30x16xf32>
}

// -----

      TCresVTEtIsSameAsOpBase<0, 2>>,
    Pure]> {
  let summary = "Dilation operator";

  let description = "Extends a tensor by adding new elements between the existing ones.";

  let arguments = (ins
     TFL_TensorOf<[I8, I16, I32, I64, UI8, UI16, UI32, UI64, F32, F64]>:$input,
     TFL_TensorOf<[I32]>:$dilations,
     TFL_0DTensorOf<[I8, I16, I32, I64, UI8, UI16, UI32, UI64, F32, F64]>:$padding_value
  );

    // CHECK: tf.Conv2DBackpropInput
    // CHECK-SAME: (tensor<4xi32>, tensor<3x3x32x64xf32>, tensor<200x24x24x64xf32>) -> tensor<200x26x26x32xf32>
    %1 = "tf.Conv2DBackpropInput"(%0, %arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true} : (tensor<4xi32>, tensor<3x3x32x64xf32>, tensor<200x24x24x64xf32>) -> tensor<?x?x?x?xf32>
    func.return %1 : tensor<?x?x?x?xf32>