std::vector<std::unique_ptr<Thread>> threads;
  threads.reserve(callers);
  for (int i = 0; i < callers; i++) {
    threads.emplace_back(
        Env::Default()->StartThread({}, "caller", [block_size, &cache, i]() {
          std::vector<char> out;
          TF_EXPECT_OK(
              ReadCache(&cache, "a", i * block_size, block_size, &out));
          std::vector<char> x(block_size, 'x');

# their dependencies. It's a rewritten version of the "smoke test", an older
# Python script that was very difficult to understand. See
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/pip_smoke_test.py
@test "Pip package includes all required //tensorflow dependencies" {
  # grep patterns for packages whose dependencies can be ignored

*   [Python license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn.py#L1)
*   [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1)
*   [Go license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/go/operation.go#L1)

typedef struct TFE_TensorHandle TFE_TensorHandle;

TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandle(const TF_Tensor* t,
                                                            TF_Status* status);
// Indicates that the caller will not be using `h` any more.
TF_CAPI_EXPORT extern void TFE_DeleteTensorHandle(TFE_TensorHandle* h);
TF_CAPI_EXPORT extern TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h);

  void (*deallocator)(void* data);

  // Summarizes the value of this tensor. The caller takes ownership of the
  // returned buffer. If `status` is not TF_OK, instead returns a null pointer.
  //
  // Does not include the shape and dtype of the tensor (which is generally
  // appended later), but should include any information specific to this custom
  // device which would be useful for debugging.
  //

//   * increased security as plugins will not be able to alter function table
//     after loading up. Thus, malicious plugins can't alter functionality to
//     probe for gadgets inside core TensorFlow. We can even protect the area
//     of memory where the copies reside to not allow any more writes to it
//     after all copies are created.
template <typename T>

  // Configure graph collection in RunMetadata.
  virtual void SetShouldStoreGraphs(bool value) = 0;

  // Return the collected RunMetadata. This method will transfer the ownership
  // to the caller.
  virtual std::unique_ptr<RunMetadata> ExportRunMetadata() = 0;

  // For LLVM style RTTI.
  static bool classof(const AbstractContext* ptr) {
    return ptr->getKind() == kEager || ptr->getKind() == kTfrt;
  }

  /// be accessed while holding the Block's mu lock. The data vector should only
  /// be accessed after state == FINISHED, and it should never be modified.
  ///
  /// In order to prevent deadlocks, never grab the block-cache-wide mu_ lock
  /// AFTER grabbing any block's mu lock. It is safe to grab mu without locking
  /// mu_.
  struct Block {
    /// The block data.

# their dependencies. It's a rewritten version of the "smoke test", an older
# Python script that was very difficult to understand. See
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/pip_smoke_test.py
@test "Pip package includes all required //tensorflow dependencies" {
  # grep patterns for packages whose dependencies can be ignored

  //
  // Like GradientTape::RecordOperation, this method takes the operation type
  // `op_type` (e.g. "Add"), the operation's inputs (`input_tensors`,
  // `input_tensor_id`, and `input_dtypes`; the latter two are somewhat
  // redundant but taken as arguments to avoid repeatedly fetching these values
  // between calls to ShouldRecord and Accumulator), and its outputs
  // (`output_tensors`).
  //