^bb0(%arg0: tensor<? x ? x ? x ? x !quant.uniform<u8:f32, 0.1>>, %arg1: tensor<? x i32>):
  %0 = "tfl.resize_nearest_neighbor"(%arg0, %arg1) { align_corners = false, half_pixel_centers = false } : (tensor<? x ? x ? x ? x !quant.uniform<u8:f32, 0.1>>, tensor<? x i32>) -> tensor<? x !quant.uniform<u8:f32, 0.1>>

                      llvm::cl::Optional, llvm::cl::init(false));

// NOLINTNEXTLINE
opt<bool> skip_resize(
    "skip-resize",
    llvm::cl::desc(
        "Skip converting tf.ResizeBilinear and tf.ResizeNearestNeighbor ops."),
    llvm::cl::Optional, llvm::cl::init(true));

// NOLINTNEXTLINE
opt<bool> smuggle_disallowed_ops(
    "smuggle-disallowed-ops",

  // b/135640736: MatrixInverse performance issues.
  // b/111271662: MatrixSolve performance issues.
  // https://github.com/tensorflow/tensorflow/pull/31012:
  //    ResizeNearestNeighbor, ResizeBilinear, and ResizeBilinearGrad sometimes
  //    create convolutions too large for CuDNN to handle.
  return node.type_string() == "SelfAdjointEigV2" ||

  // CHECK: [[VAL5:%.+]] = "tf.GatherV2"([[VAL4]], [[VAL3]], [[VAL0]]) <{batch_dims = 0 : i64}>
  // CHECK: [[VAL6:%.+]] = "tf.Reshape"([[VAL5]], [[VAL1]])
  // CHECK: return [[VAL6]]
  %resize = "tf.ResizeNearestNeighbor"(%arg0, %shape) {align_corners = false, device = "", half_pixel_centers = false} : (tensor<1x7x7x1xi32>, tensor<2xi32>) -> tensor<1x3x3x1xi32>
  func.return %resize: tensor<1x3x3x1xi32>
}

    "LOGICAL_NOT",
    "UNPACK",
    "REDUCE_MIN",
    "FLOOR_DIV",
    "REDUCE_ANY",
    "SQUARE",
    "ZEROS_LIKE",
    "FILL",
    "FLOOR_MOD",
    "RANGE",
    "RESIZE_NEAREST_NEIGHBOR",
    "LEAKY_RELU",
    "SQUARED_DIFFERENCE",
    "MIRROR_PAD",
    "ABS",
    "SPLIT_V",
    "UNIQUE",
    "CEIL",
    "REVERSE_V2",
    "ADD_N",
    "GATHER_ND",

      Operation *op = rewriter.create(state);
      result = op->getResult(0);
    }
    rewriter.replaceOp(op, {result});
    return success();
  }
};

// Lowers ResizeNearestNeighbor to an indices computations with a gather along
// the combined spatial dimensions. Generating the indices along the
// width/height index could be used to gather along each of W and H dimension

      "RandomStandardNormal",
      "RandomUniform",
      "RandomUniformInt",
      "ReadVariableOp",
      "ReadVariableXlaSplitND",
      "ResizeBilinear",
      "ResizeBilinearGrad",
      "ResizeNearestNeighbor",
      "ResourceApplyAdaMax",
      "ResourceApplyAdadelta",
      "ResourceApplyAdagrad",
      "ResourceApplyAdagradDA",
      "ResourceApplyAdagradV2",
      "ResourceApplyAdam",

*   CoreML
    *   Opensource CoreML delegate
*   Misc
    *   Enable building Android TFLite targets on Windows
    *   Add support for `BatchMatMul`.
    *   Add support for `half_pixel_centers` with `ResizeNearestNeighbor`.
    *   Add 3D support for `BatchToSpaceND`.
    *   Add 5D support for `BroadcastSub`, `Maximum`, `Minimum`, `Transpose` and
        `BroadcastDiv`.
    *   Rename `kTfLiteActRelu1` to `kTfLiteActReluN1To1`.

float or double.}]>:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<1>;
}

def TF_ResizeNearestNeighborOp : TF_Op<"ResizeNearestNeighbor", [Pure]> {
  let summary = [{
Resize `images` to `size` using nearest neighbor interpolation.
  }];

  let arguments = (ins