-   Ensure you have signed the
    [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
-   Check if your changes are consistent with the
    [guidelines](#general-guidelines-and-philosophy-for-contribution).
-   Changes are consistent with the [Coding Style](#c-coding-style).
-   Run the [unit tests](#running-unit-tests).

## How to become a contributor and submit your own code

  // attributes. Number type attrs and DataType attrs work fine without this.
  // Consider getting rid of this and making the behavior between number types
  // and string consistent.
  forward_op_->attrs.BuildNodeDef();
  std::unique_ptr<GradientFunction> gradient_fn;
  TF_RETURN_IF_ERROR(registry.Lookup(*forward_op_, &gradient_fn));

      return errors::FailedPrecondition(
          "GraphOperation::Reset must be called before calling SetOpName.");
    }
    // TODO(b/145674566): We use Graph::NewName to get a unique name here but
    // this may not be consistent with python's naming policy.
    mutex_lock l(g_->mu);
    op_.reset(new TF_OperationDescription(g_, op_type_.c_str(),
                                          g_->graph.NewName(op_name).c_str()));

  auto posix_file = static_cast<PosixFile*>(file->plugin_file);
  close(posix_file->fd);
  // This would be safe to free using `free` directly as it is only opaque.
  // However, it is better to be consistent everywhere.
  plugin_memory_free(const_cast<char*>(posix_file->filename));
  delete posix_file;
}

static int64_t Read(const TF_RandomAccessFile* file, uint64_t offset, size_t n,

                                                     bool use_tfrt);

// Returns the context_id from the EagerContext which is used by the
// EagerService to maintain consistency between client and worker. The
// context_id is initialized with a dummy value and is later set when the worker
// is initialized (either locally or remotely). The context_id can change during

    if (gcs_file->stat_cache->Lookup(path, &stat)) {
      if (offset + read < stat.base.length) {
        TF_SetStatus(status, TF_INTERNAL,
                     absl::StrCat("File contents are inconsistent for file: ",
                                  path, " @ ", offset)
                         .c_str());
      }
      TF_VLog(2, "Successful integrity check for: %s @ %u", path.c_str(),
              offset);

                                                       TFE_TensorHandle* tensor,
                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

                        const int in_flight_nodes_limit)
      : status_(TF_NewStatus()),
        // If the context's default exector is set to async, re-using that in
        // each thread would cause collectives to deadlock. For consistency we
        // create a new sync executor for every thread.
        //
        // TODO(allenl): We should have an async API that works with the
        // parallel device.
        device_(device),
        executor_(