namespace {

std::vector<int64_t> TensorShapeAsVector(const tensorflow::TensorHandle& handle,
                                         absl::Status* status) {
  std::vector<int64_t> shape;
  int rank = -1;
  *status = handle.NumDims(&rank);
  if (!status->ok()) {
    return shape;
  }
  shape.reserve(rank);
  for (int i = 0; i < rank; ++i) {
    int64_t dim;
    *status = handle.Dim(i, &dim);
    if (!status->ok()) {
      return shape;

    LsarSidArrayX(final jcifs.SID[] sids) {
        this.num_sids = sids.length;
        this.sids = new lsarpc.LsarSidPtr[sids.length];
        for (int si = 0; si < sids.length; si++) {
            this.sids[si] = new lsarpc.LsarSidPtr();
            this.sids[si].sid = sids[si].unwrap(sid_t.class);
        }
    }

    LsarSidArrayX(final SID[] sids) {
        this.num_sids = sids.length;

package jcifs.smb1.dcerpc.msrpc;

import jcifs.smb1.smb1.SID;

class LsarSidArrayX extends lsarpc.LsarSidArray {

    LsarSidArrayX(final SID[] sids) {
        this.num_sids = sids.length;
        this.sids = new lsarpc.LsarSidPtr[sids.length];
        for (int si = 0; si < sids.length; si++) {
            this.sids[si] = new lsarpc.LsarSidPtr();
            this.sids[si].sid = sids[si];
        }
    }

        lsarpc.LsarSidArray decodedArray = new lsarpc.LsarSidArray();
        decodedArray.decode(decodeBuffer);

        // Verify the decoded data matches
        assertEquals(sidArray.num_sids, decodedArray.num_sids);
        assertNotNull(decodedArray.sids);
        assertEquals(2, decodedArray.sids.length);

        // Verify first SID
        assertNotNull(decodedArray.sids[0]);

        sidArray.num_sids = 2;
        sidArray.sids = new lsarpc.LsarSidPtr[2];
        sidArray.sids[0] = mock(lsarpc.LsarSidPtr.class);
        sidArray.sids[1] = mock(lsarpc.LsarSidPtr.class);

        sidArray.encode(mockNdrBuffer);

        verify(mockNdrBuffer).align(4);
        verify(mockNdrBuffer).enc_ndr_long(sidArray.num_sids);

        }

        /** Number of SIDs in the array. */
        public int num_sids;
        /** Array of SID pointers. */
        public LsarSidPtr[] sids;

        @Override
        public void encode(NdrBuffer _dst) throws NdrException {
            _dst.align(4);
            _dst.enc_ndr_long(this.num_sids);
            _dst.enc_ndr_referent(this.sids, 1);

            if (this.sids != null) {

        }

        /** Number of SIDs in the array. */
        public int num_sids;
        /** Array of SID pointers. */
        public LsarSidPtr[] sids;

        @Override
        public void encode(NdrBuffer _dst) throws NdrException {
            _dst.align(4);
            _dst.enc_ndr_long(num_sids);
            _dst.enc_ndr_referent(sids, 1);

            if (sids != null) {

                    if (rpc.retval != 0) {
                        throw new SmbException(rpc.retval, false);
                    }
                    final SID[] sids = new SID[rpc.sids.num_sids];

                    final String origin_server = handle.getServer();
                    final CIFSContext origin_ctx = handle.getTransportContext();

                    for (int i = 0; i < sids.length; i++) {

            handle.sendrecv(rpc);
            if (rpc.retval != 0) {
                throw new SmbException(rpc.retval, false);
            }
            final SID[] sids = new SID[rpc.sids.num_sids];

            final String origin_server = handle.getServer();
            final NtlmPasswordAuthentication origin_auth = (NtlmPasswordAuthentication) handle.getPrincipal();

        *   `CategoryEncoding` no longer supports `adapt`, `IntegerLookup` now
            supports equivalent functionality. `max_tokens` argument renamed to
            `num_tokens`.
        *   `Discretization` added `num_bins` argument for learning bins
            boundaries through calling `adapt` on a dataset. Renamed `bins`
            argument to `bin_boundaries` for specifying bins without `adapt`.