}

func.func @resize_with_bilinear(%arg0: tensor<1x100x100x3xf32>, %arg1: tensor<4xi32>) -> tensor<?xf32> {
  %0 = "tf.ResizeBilinear"(%arg0, %arg1) {align_corners = true} : (tensor<1x100x100x3xf32>, tensor<4xi32>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>
  // CHECK-LABEL: resize_with_bilinear

      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    TFL_OperandHasRank<0, 4>,
    TFL_OperandHasRank<1, 1>,
    SameOperandsAndResultsScale]> {
  let summary = "ResizeBilinear Op";

  let description = [{
    Resize `images` to `size` using bilinear interpolation.
  }];

  let arguments = (ins
    TFL_TensorOf<[F32, TFL_Quint8, QUI8, QI8, QI16]>:$input,

    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`.