SINGLE_CASE(kF, TF_ATTR_FLOAT, -1);
    SINGLE_CASE(kB, TF_ATTR_BOOL, -1);
    SINGLE_CASE(kType, TF_ATTR_TYPE, -1);
    SINGLE_CASE(kShape, TF_ATTR_SHAPE,
                attr->shape().unknown_rank() ? -1 : attr->shape().dim_size());
    SINGLE_CASE(kTensor, TF_ATTR_TENSOR, -1);
#undef SINGLE_CASE

    case tensorflow::AttrValue::kList:
      metadata.is_list = 1;
      metadata.list_size = 0;

                       const char* dst, TF_Status* status) {
  // If target is a directory return TF_FAILED_PRECONDITION.
  // Target might be missing, so don't error in that case.
  struct stat st;
  if (stat(dst, &st) != 0) {
    if (errno != ENOENT) {
      TF_SetStatusFromIOError(status, errno, dst);
      return;
    }
  } else if (S_ISDIR(st.st_mode)) {

       ++device_index) {
    DeviceThread* device_thread = device_threads_[device_index].get();
    per_device_output_tensors.push_back(device_thread->Join(status));
    // We will run every Join even if there are bad statuses in case the user
    // wants to recover and continue running ops on the parallel device (which
    // would otherwise deadlock).
    if (TF_GetCode(status) != TF_OK &&
        (first_bad_status == nullptr

  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).
  // Setting a larger thread pool does not help with the Swift caller, as we use

  ASSERT_TF_OK(status_);
  const std::string base = GetURIForPath("GetChildren");
  tf_gcs_filesystem::CreateDir(filesystem_, base.c_str(), status_);
  EXPECT_TF_OK(status_);

  const std::string file = io::JoinPath(base, "TestFile.csv");
  WriteString(file, "test");
  EXPECT_TF_OK(status_);

  const std::string subdir = io::JoinPath(base, "SubDir");

        }
        block->cond_var.SignalAll();
        return;
      case FetchState::FETCHING:
        block->cond_var.WaitWithTimeout(&block->mu, absl::Minutes(1));
        if (block->state == FetchState::FINISHED) {
          return TF_SetStatus(status, TF_OK, "");
        }
        // Re-loop in case of errors.
        break;
      case FetchState::FINISHED:
        return TF_SetStatus(status, TF_OK, "");
    }
  }

  switch (data_type) {
    case TF_DataType::TF_BOOL:
      dtype.code = DLDataTypeCode::kDLBool;
      break;
    case TF_DataType::TF_HALF:
    case TF_DataType::TF_FLOAT:
    case TF_DataType::TF_DOUBLE:
      dtype.code = DLDataTypeCode::kDLFloat;
      break;
    case TF_DataType::TF_INT8:
    case TF_DataType::TF_INT16:
    case TF_DataType::TF_INT32:
    case TF_DataType::TF_INT64:

    calls++;
    char c = (filename == "a") ? 'a' : (filename == "b") ? 'b' : 'x';
    if (offset > 0) {
      // The first block is lower case and all subsequent blocks are upper case.
      c = toupper(c);
    }
    memset(buffer, c, n);
    TF_SetStatus(status, TF_OK, "");
    return n;
  };
  // This cache has space for 4 blocks; we'll read from two files.

                                   status.get()),
      TFE_DeleteTensorHandle);
  ASSERT_TRUE(arrived);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  // Base case: two CPU inputs executes fine.
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> matmul(
      MatMulOp(context.get(), hcpu.get(), hcpu.get()), TFE_DeleteOp);
  TFE_TensorHandle* retval;
  int num_retvals = 1;

  TFE_DeleteOp(original_var_op);
  TFE_DeleteOp(cloned);
  TF_DeleteStatus(status);
  TFE_DeleteContext(ctx);
}

TEST(CAPI, ShareVariableAcrossContextsWorks) {
  // TODO(shreepadma): Add a test case with isolate_session_state set to true.
  tensorflow::ServerDef server_def_0 = GetServerDef(3);
  server_def_0.mutable_default_session_config()->set_isolate_session_state(
      false);