%5 = "tf.Cast"(%1) {Truncate = false} : (tensor<1x3x4x3xi32>) -> tensor<1x3x4x3xf32>
    %6 = "tf.Cast"(%3) {Truncate = false} : (tensor<2x3x3x1xi32>) -> tensor<2x3x3x1xf32>
    %7 = "tf.DepthwiseConv2dNative"(%5, %6) {dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1]} : (tensor<1x3x4x3xf32>, tensor<2x3x3x1xf32>) -> tensor<1x2x2x3xf32>

      "Cross",
      "Cumprod",
      "Cumsum",
      "CumulativeLogsumexp",
      "DenseBincount",
      "DataFormatDimMap",
      "DataFormatVecPermute",
      "DepthToSpace",
      "DepthwiseConv2dNative",
      "DepthwiseConv2dNativeBackpropFilter",
      "DepthwiseConv2dNativeBackpropInput",
      "Dequantize",
      "Diag",
      "DynamicInfeedEnqueueTupleOp",
      "DynamicInfeedDequeueTupleOp",

func.func @testValidDepthwiseConv2dNative(tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>) -> tensor<256x30x30x12xf32> {
^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x4xf32>) :

// CHECK:           %[[VAL_2:.*]] = "tf.Reshape"(%[[VAL_1]], %[[CST]]) : (tensor<3x3x1x3312xf32>, tensor<4xi64>) -> tensor<3x3x207x16xf32>
// CHECK:           %[[VAL_3:.*]] = "tf.DepthwiseConv2dNative"(%[[VAL_0]], %[[VAL_2]]) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1]}> : (tensor<1x8x8x207xf32>, tensor<3x3x207x16xf32>) -> tensor<1x8x8x3312xf32>

  // CHECK: %[[RESHAPED_FILTER:.*]] = mhlo.reshape %arg1 : (tensor<2x2x3x3xf32>) -> tensor<2x2x1x9xf32>
  // CHECK: mhlo.convolution(%arg0, %[[RESHAPED_FILTER]])
  // CHECK-SAME: feature_group_count = 3
  %0 = "tf.DepthwiseConv2dNative"(%arg0, %arg1) {
    data_format = "NHWC",
    device = "",
    dilations = [1, 1, 1, 1],
    explicit_paddings = [],
    padding = "VALID",
    strides = [1, 1, 1, 1]

  );

  let results = (outs
    TF_Tensor:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_DepthwiseConv2dNativeOp : TF_Op<"DepthwiseConv2dNative", [Pure]> {
  let summary = [{
Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
  }];

  let description = [{