gccfiles = append(gccfiles, cfiles...)
		cfiles = nil
		if p.Standard && p.ImportPath == "runtime/cgo" {
			filter := func(files, nongcc, gcc []string) ([]string, []string) {
				for _, f := range files {
					if strings.HasPrefix(f, "gcc_") {
						gcc = append(gcc, f)
					} else {
						nongcc = append(nongcc, f)
					}
				}
				return nongcc, gcc
			}
			sfiles, gccfiles = filter(sfiles, sfiles[:0], gccfiles)

//
// values, indices = tfl.topkv2(%inputs, K)
// %1 = tfl.slice(values, 0, k)
// %2 = tfl.slice(indices,0, k)
// .... (values and indices only used for %1 and %2)
//
// %1 or %2 can be absent. If values and indices are only used here,
// this pattern can be replaced with (conceptually)
//
// %values, %indices = tfl.topkv2(%inputs, k)
// replace all use of %1 with values

                "of indices");
    }
  }

  if ((indices_rank == 0) || (indices_rank == batch_dims_i)) {
    // Scalar indices (output is rank(params) - 1).
    // Erase shape[axis]
    shape.erase(shape.begin() + axis_i);
  } else if (indices_rank == 1) {
    // Vector indices (output is rank(params)).
    // Copy indices.shape into params.shape[axis]

}

void OneHotOp::build(OpBuilder &builder, OperationState &result, Value indices,
                     Value depth, Value on_value, Value off_value,
                     IntegerAttr axis) {
  build(builder, result,
        InferOneHotOpType(indices, depth, on_value, off_value, axis), indices,
        depth, on_value, off_value, axis);
}

  // CHECK:  %values, %indices = "tfl.topk_v2"(%arg0, %cst) : (tensor<1x4xf32>, tensor<i32>) -> (tensor<1x4xf32>, tensor<1x4xi32>)
  // CHECK:  return %values, %indices : tensor<1x4xf32>, tensor<1x4xi32>
}

func.func @approx_top_k_with_min_k(%arg0: tensor<1x4xf32>) -> (tensor<1x4xf32>, tensor<1x4xi32>) {

                             .Union();
      }

      auto indices = dim_metadata.getIndices();
      std::vector<int> vector_indices(indices.size(), 0);
      int max_of_indices = 0;
      for (int j = 0, end = indices.size(); j < end; j++) {
        vector_indices[j] = indices[j];
        if (vector_indices[j] > max_of_indices) {
          max_of_indices = vector_indices[j];

               PatternRewriter& rewriter) const override {
    rewriter.replaceOpWithNewOp<TFL::GatherNdOp>(
        op, /*output=*/op.getResult().getType(), /*params=*/op.getOperand(),
        /*indices=*/op.getStartIndices());
  }
};

// Rewrites quantized stablehlo.dynamic_slice to tfl.slice.
// TODO: b/322428814 - Add StableHLO quantizer integration tests for ODML.
class RewriteQuantizedDynamicSliceOp

    %0 = "tf.Shape"(%arg0) : (tensor<?x1x7xf32>) -> tensor<3xi32>

    %indices = "tf.Const"() {value = dense<[0, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
    %axis = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<i32>
    %1 = "tf.GatherV2"(%0, %indices, %axis) {batch_dims = 0 : i64, device = ""} : (tensor<3xi32>, tensor<2xi32>, tensor<i32>) -> tensor<2xi32>

    /** The key of the message: Crawler Indices */
    public static final String LABELS_clear_crawler_index = "{labels.clear_crawler_index}";

    /** The key of the message: Clear Crawler Indices */
    public static final String LABELS_clear_crawler_index_button = "{labels.clear_crawler_index_button}";

}

func (i Machine) String() string   { return stringName(uint32(i), machineStrings, false) }
func (i Machine) GoString() string { return stringName(uint32(i), machineStrings, true) }

// Special section indices.
type SectionIndex int

const (
	SHN_UNDEF     SectionIndex = 0      /* Undefined, missing, irrelevant. */
	SHN_LORESERVE SectionIndex = 0xff00 /* First of reserved range. */