if (finish < offset + n) {
    finish += block_size_;
  }
  size_t total_bytes_transferred = 0;
  // Now iterate through the blocks, reading them one at a time.
  for (size_t pos = start; pos < finish; pos += block_size_) {
    Key key = std::make_pair(filename, pos);
    // Look up the block, fetching and inserting it if necessary, and update the
    // LRU iterator for the key and block.
    std::shared_ptr<Block> block = Lookup(key);

/tensorflow/lite/**/ios/BUILD
/tensorflow/lite/**/objc/BUILD
/tensorflow/lite/**/swift/BUILD
/tensorflow/lite/examples/ios/simple/data/*.tflite
/tensorflow/lite/examples/ios/simple/data/*.txt
Podfile.lock
Pods

  // Test iterating through the nodes of a graph.
  found_placeholder = false;
  found_scalar_const = false;
  found_add = false;
  bool found_neg = false;
  size_t pos = 0;
  TF_Operation* oper;
  while ((oper = TF_GraphNextOperation(graph, &pos)) != nullptr) {
    if (oper == feed) {
      EXPECT_FALSE(found_placeholder);
      found_placeholder = true;
    } else if (oper == three) {

                               ? std::get<std::string>(value)
                               : std::get<gcs::ObjectMetadata>(value).name();
    auto pos = children.find(prefix);
    if (pos != 0) {
      TF_SetStatus(status, TF_INTERNAL,
                   absl::StrCat("Unexpected response: the returned file name ",
                                children, " doesn't match the prefix ", prefix)

        multiple of the number of replicas.
    *   `tf.data.Dataset` now supports automatic data distribution and sharding
        in distributed environments, including on TPU pods.
    *   Distribution policies for `tf.data.Dataset` can now be tuned with 1.
        `tf.data.experimental.AutoShardPolicy(OFF, AUTO, FILE, DATA)` 2.
        `tf.data.experimental.ExternalStatePolicy(WARN, IGNORE, FAIL)`

    // executions not forwarded to backward_tape should be ignored.
    bool accumulating;
  };
  // A deque-backed stack, whose element references are not invalidated by
  // pushes and pops at the back.
  std::stack<AccumulatorCallState> call_state_;
};

// Template instantiations here

inline bool IsDtypeTrainable(DataType dtype) {
  switch (dtype) {
    case DT_HALF:

// Iterate through the operations of a graph.  To use:
// size_t pos = 0;
// TF_Operation* oper;
// while ((oper = TF_GraphNextOperation(graph, &pos)) != nullptr) {
//   DoSomethingWithOperation(oper);
// }
TF_CAPI_EXPORT extern TF_Operation* TF_GraphNextOperation(TF_Graph* graph,
                                                          size_t* pos);

// Write out a serialized representation of `graph` (as a GraphDef protocol