CHECK_EQ(output_shapes->num_items, 1);

    int num_dims = output_shapes->items[0].num_dims;
    int64_t* dims = output_shapes->items[0].dims;

    if (!expected_shape.has_value()) {
      EXPECT_EQ(num_dims, -1);
      EXPECT_EQ(dims, nullptr);
      return;
    }

    EXPECT_EQ(num_dims, expected_shape->size());
    for (size_t i = 0; i < num_dims; ++i) {
      EXPECT_EQ(dims[i], (*expected_shape)[i]);
    }

                                 const int64_t* dims, int num_dims) {
  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape = shape_list->items[index];
  DCHECK(shape.dims == nullptr) << "Shape at " << index << " is already set!";
  DCHECK(num_dims >= 0) << "Number of dimensions cannot be negative!";
  shape.num_dims = num_dims;
  shape.dims = new int64_t[num_dims];
  memcpy(shape.dims, dims, sizeof(int64_t) * num_dims);

                                                    TF_Status* status);

// Fills in `dims` with the list of shapes in the attribute `attr_name` of
// `oper` and `num_dims` with the corresponding number of dimensions. On return,
// for every i where `num_dims[i]` > 0, `dims[i]` will be an array of
// `num_dims[i]` elements. A value of -1 for `num_dims[i]` indicates that the

TEST(CAPI, TestTensorNonScalarBytesAllocateDelete) {
  const int batch_size = 4;
  const int num_dims = 2;
  int64_t* dims = new int64_t[num_dims];
  int64_t num_elements = 1;
  dims[0] = batch_size;
  dims[1] = 1;
  for (int64_t i = 0; i < num_dims; ++i) {
    num_elements *= dims[i];
  }
  TF_Tensor* t = TF_AllocateTensor(TF_STRING, dims, num_dims,
                                   sizeof(TF_TString) * num_elements);

tensorflow::shape_inference::ShapeHandle ShapeHandleFromDims(
    tensorflow::shape_inference::InferenceContext* ic, int num_dims,
    const int64_t* dims) {
  if (num_dims != -1) {
    std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
    dim_vec.reserve(num_dims);
    for (int i = 0; i < num_dims; ++i) {
      dim_vec.push_back(ic->MakeDim(dims[i]));
    }
    return ic->MakeShape(dim_vec);
  } else {

// Read the shape of a variable and write to `dims`
TF_CAPI_EXPORT extern void TF_CheckpointReaderGetVariableShape(
    TF_CheckpointReader* reader, const char* name, int64_t* dims, int num_dims,
    TF_Status* status);
// Get the number of dimension of a variable
TF_CAPI_EXPORT extern int TF_CheckpointReaderGetVariableNumDims(
    TF_CheckpointReader* reader, const char* name);
// Load the weight of a variable

    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;

        jcifs.SID[] sids = { mockSid1, mockSid2 };
        LsarSidArrayX lsarSidArrayX = new LsarSidArrayX(sids);

        // Verify num_sids
        assertEquals(2, lsarSidArrayX.num_sids, "num_sids should match the array length");

        // Verify sids array and its contents
        assertNotNull(lsarSidArrayX.sids, "sids array should not be null");

        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]);

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];
        }
    }