char* buffer, TF_Status* status) -> int64_t {
    int64_t bytes_transferred;
    if (offset < buf.size()) {
      size_t bytes_to_copy = std::min<size_t>(buf.size() - offset, n);
      memcpy(buffer, buf.data() + offset, bytes_to_copy);
      bytes_transferred = bytes_to_copy;
    } else {
      bytes_transferred = 0;
    }
    TF_SetStatus(status, TF_OK, "");

                            const string& op_device, TF_Status* s,
                            const char* name) {
  TF_Operation* op;
  BinaryOpHelper("Min", l, r, graph, s, name, &op, op_device, true);
  return op;
}

TF_Operation* Min(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                  TF_Status* s, const char* name) {
  return MinWithDevice(l, r, graph, /*op_device=*/"", s, name);
}

template <typename T>
static std::unique_ptr<const T> CopyToCore(const T* plugin_ops,
                                           size_t plugin_size) {
  if (plugin_ops == nullptr) return nullptr;

  size_t copy_size = std::min(plugin_size, sizeof(T));
  auto core_ops = std::make_unique<T>();
  memset(core_ops.get(), 0, sizeof(T));
  memcpy(core_ops.get(), plugin_ops, copy_size);
  return core_ops;
}

    size_t buffer_end = gcs_file->buffer_start + gcs_file->buffer.size();
    size_t copy_size = 0;
    if (offset < buffer_end && gcs_file->buffer_start) {
      copy_size = (std::min)(n, static_cast<size_t>(buffer_end - offset));
      memcpy(buffer,
             gcs_file->buffer.data() + (offset - gcs_file->buffer_start),
             copy_size);
    }
    bool consumed_buffer_to_eof =

  return th;
}

TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input,
              TFE_TensorHandle* axis) {
  TF_Status* status = TF_NewStatus();

  TFE_Op* op = TFE_NewOp(ctx, "Min", status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, input, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, axis, status);

    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =
        tensorflow::GetMarkForCompilationPassFlags();
    flags->tf_xla_cpu_global_jit = true;
    flags->tf_xla_min_cluster_size = 1;
  } else {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::OFF);
  }
}

unsigned char TF_SetXlaEnableLazyCompilation(unsigned char enable) {

        break
      k -= 1

    # A low-effort attempt to find the bazel command that triggered the
    # invocation.
    bazel_comm_min_line_i = (previous_end_line if previous_end_line is not None
                             else 0)
    while k > bazel_comm_min_line_i:
      backtrack_line = log_lines[k]
      # Don't attempt to parse multi-line commands broken up by backslashes

                         TF_Status* status) {
  tensorflow::FunctionDefLibrary lib;
  {
    tensorflow::mutex_lock l(g->mu);
    lib = g->graph.flib_def().ToProto();
  }
  const auto len = std::min(max_func, static_cast<int>(lib.function_size()));
  for (int i = 0; i < len; ++i) {
    TF_Function* func = new TF_Function();
    func->record = new tensorflow::FunctionRecord(lib.function(i), {}, false);
    funcs[i] = func;