int64_t local_rendezvous_cache_active_size;
  };
  virtual CacheStats GetCacheStats() = 0;

  //===--------------------------------------------------------------------===//
  // Distributed runtime related functions.
  //===--------------------------------------------------------------------===//
#if !defined(IS_MOBILE_PLATFORM)
  // Set up a multi-client distributed execution environment. Must be called on

  TF_Output output{dequeue_op, 0};
  TF_Tensor* ret;
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,
                // output related parameters
                /*outputs*/ &output, /*output_values*/ &ret,
                /*noutputs*/ 1,
                /*targets*/ nullptr, /*ntargets*/ 0,

TF_ImportGraphDefOptionsSetValidateColocationConstraints(
    TF_ImportGraphDefOptions* opts, unsigned char enable);

// Load the library specified by library_filename and register the pluggable
// device and related kernels present in that library. This function is not
// supported on embedded on mobile and embedded platforms and will fail if
// called.
//
// Pass "library_filename" to a platform-specific mechanism for dynamically

using OpTape =
    std::unordered_map<int64_t, OpTapeEntry<BackwardFunction, TapeTensor>>;

// Operations the tape needs to perform on tensors to do backpropagation. Named
// "vspace" because a subset of these are related to a vector space, such as
// adding gradients, getting zeroes, etc. Currently cannot be implemented
// without using tensorflow python code, hence left unspecified here.
//

    // would otherwise deadlock).
    if (TF_GetCode(status) != TF_OK &&
        (first_bad_status == nullptr
         // Prefer propagating non-cancellation related statuses to avoid
         // shadowing the original failure.
         || TF_GetCode(first_bad_status.get()) == TF_CANCELLED)) {
      first_bad_status.reset(TF_NewStatus());