"LOCAL_RESPONSE_NORMALIZATION",
    "LOGISTIC",
    "LSH_PROJECTION",
    "LSTM",
    "MAX_POOL_2D",
    "MUL",
    "RELU",
    "RELU_N1_TO_1",
    "RELU6",
    "RESHAPE",
    "RESIZE_BILINEAR",
    "RNN",
    "SOFTMAX",
    "SPACE_TO_DEPTH",
    "SVDF",
    "TANH",
    "CONCAT_EMBEDDINGS",
    "SKIP_GRAM",
    "CALL",
    "CUSTOM",
    "EMBEDDING_LOOKUP_SPARSE",

// CHECK-DAG:       %[[SIZE:.*]] = "tf.Const"() <{value = dense<[56, 904]> : tensor<2xi32>}> : () -> tensor<2xi32>
// CHECK:           %[[VAL_2:.*]] = "tf.ResizeBilinear"(%[[VAL_0]], %[[SIZE]]) <{align_corners = true, half_pixel_centers = false}> : (tensor<1x56x624x16xf32>, tensor<2xi32>) -> tensor<1x56x904x16xf32>
// CHECK:           return %[[VAL_2]] : tensor<1x56x904x16xf32>

    OpBuilder<(ins "Value":$tensor, "Value":$shape)>
  ];

  let hasVerifier = 1;

  let hasCanonicalizer = 1;
  let hasFolder = 1;
}

def TF_ResizeBilinearOp : TF_Op<"ResizeBilinear", [Pure]> {
  let summary = "Resize `images` to `size` using bilinear interpolation.";

  let description = [{
Input images can be of different types but output images are always float.
  }];

        is now supported and enabled by default. This adds support for more
        TensorFlow ops including Conv3D, Conv3DBackpropInputV2, AvgPool3D,
        MaxPool3D, ResizeBilinear, and ResizeNearestNeighbor. In addition, the
        TensorFlow-TensorRT python conversion API is exported as
        `tf.experimental.tensorrt.Converter`.