TF_EXPECT_OK(ReadCache(&cache, "", 2 * block_size, 1, &out));
  // LRU element was at offset 0.  Cache miss.
  calls.push_back(0);
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
  // Element at 2 * block_size is still in cache, and this read should update
  // its position in the LRU list so it doesn't get evicted by the next read.
  TF_EXPECT_OK(ReadCache(&cache, "", 2 * block_size, 1, &out));

  // in the cache, and our current block is not block size, this likely means
  // we have inconsistent state within the cache. Note: it's possible some
  // incomplete reads may still go undetected.
  if (block->data.size() < block_size_) {
    Key fmax = std::make_pair(key.first, std::numeric_limits<size_t>::max());
    auto fcmp = block_map_.upper_bound(fmax);

//       `struct_size` as well, along with all other fields.
//     * Refer to "TensorFlow Versioning Strategy" section at
//       https://github.com/tensorflow/community/pull/257/files.
//     * Note that the API is still under active development and doesn't have
//       versioning guarantees yet.
//   * `void* ext` is a free-form field that can be populated by
//     a plugin in `TP_*` structs or potential future extension points .
//

//   * allows having newer plugins than the current core TensorFlow: the
//     additional entries in the plugin's table are just discarded;
//   * allows having older plugins than the current core TensorFlow (though
//     we are still warning users): the entries that core TensorFlow expects
//     but plugins didn't provide will be set to `nullptr` values and core
//     TensorFlow will know to not call these on behalf of users;

struct BackpropInitialState {
  OpTape<BackwardFunction, TapeTensor> op_tape;

  // Map from tensor ID to how many references still exist for this tensor in
  // the tape.
  std::unordered_map<int64_t, int64_t> tensor_usage_counts;

  // Maps from op ID to how many output tensors of this op still need to have
  // their gradients computed.
  std::unordered_map<int64_t, int64_t> op_missing_tensor;
};

example, you can build the pip package with debugging information by running:

```bash
bazel build --config=dbg //tensorflow/tools/pip_package:build_pip_package
```

TensorFlow kernels and TensorFlow's dependencies are still not built with
debugging information with `--config=dbg`, as issues occur on Linux if
there is too much debug info (see [this GitHub
issue](https://github.com/tensorflow/tensorflow/issues/48919) for context). If

  EXPECT_TRUE(executed);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  TFE_DeleteTensorHandle(retval);

  // Explicit placement still forces the op onto the requested device
  matmul.reset(MatMulOp(context.get(), hcustom0.get(), hcpu.get()));
  TFE_OpSetDevice(matmul.get(), "/job:localhost/replica:0/task:0/device:CPU:0",
                  status.get());

                          /*expected_max_outputs=*/1, status.get());
  ASSERT_FALSE(TF_GetCode(status.get()) == TF_OK);
  TF_SetStatus(status.get(), TF_OK, "");

  // Check that ops still run successfully on the device.
  parallel_device.Execute(context.get(), std::vector<ParallelTensor*>(),
                          "VarHandleOp", TFE_OpGetAttrs(handle_op.get()),

// "fifo_queue_enqueue_<tensor_id>", to be executed by this API call. It reads
// from a placeholder node "arg_tensor_enqueue_<tensor_id>".
//
// `tensor` is still owned by the caller. This call will be blocked if the queue
// has reached its capacity, and will be unblocked when the queued tensors again
// drop below the capacity due to dequeuing.
//

//
// If the device is successfully registered, `status` is set to TF_OK. Otherwise
// the device is not usable. In case of a bad status, `device.delete_device` is
// still called on `device_info` (i.e. the caller does not retain ownership).
//
// This API is highly experimental, and in particular is expected to change when