if _os.path.exists(_kernel_dir):
    _ll.load_library(_kernel_dir)

  # Load third party dynamic kernels.
  for _s in _site_packages_dirs:
    _plugin_dir = _os.path.join(_s, "tensorflow-plugins")
    if _os.path.exists(_plugin_dir):
      _ll.load_library(_plugin_dir)
      # Load Pluggable Device Library
      _ll.load_pluggable_device_library(_plugin_dir)

# because "import tensorflow" run from the root directory would instead import
# the folder instead of the venv package.
# 
# Pass --config=pip to run the same suite of tests. If you want to run just one
# test for investigation, you'll need --config=pip_venv instead, and then you
# can specify whichever target you want.
test:pip_venv --action_env PYTHON_BIN_PATH="/bazel_pip/bin/python3"
test:pip_venv --action_env PYTHON_LIB_PATH="/bazel_pip/lib/python3/site-packages"

  EXPECT_EQ(calls, 4);
}

TEST(RamFileBlockCacheTest, BlockAlignment) {
  // Initialize a 256-byte buffer.  This is the file underlying the reads we'll
  // do in this test.
  const size_t size = 256;
  std::vector<char> buf;
  buf.reserve(size);
  for (int i = 0; i < size; i++) {
    buf.push_back(i);
  }
  // The fetcher just fetches slices of the buffer.

# because "import tensorflow" run from the root directory would instead import
# the folder instead of the venv package.
# 
# Pass --config=pip to run the same suite of tests. If you want to run just one
# test for investigation, you'll need --config=pip_venv instead, and then you
# can specify whichever target you want.
test:pip_venv --action_env PYTHON_BIN_PATH="/bazel_pip/bin/python3"
test:pip_venv --action_env PYTHON_LIB_PATH="/bazel_pip/lib/python3/site-packages"

    // since we already have a bad status to propagate.
    TFE_ContextAsyncWait(context, await_status.get());
    // Reset the cancellation manager on a bad status. Otherwise we'll cancel
    // all future operations.
    default_cancellation_manager_ = absl::make_unique<CancellationManager>();
  }
  return result;
}

void ParallelDevice::StartExecute(TFE_Context* context,

  if _os.path.exists(_kernel_dir):
    _ll.load_library(_kernel_dir)

  # Load third party dynamic kernels.
  for _s in _site_packages_dirs:
    _plugin_dir = _os.path.join(_s, "tensorflow-plugins")
    if _os.path.exists(_plugin_dir):
      _ll.load_library(_plugin_dir)
      # Load Pluggable Device Library
      _ll.load_pluggable_device_library(_plugin_dir)

        // just copy-off but includes a sum) and consideration of performance.
        //
        // TODO(allenl): There may be smarter ways to do this copy in some
        // cases, i.e. with a collective broadcast. We'll need to be careful
        // about things that are taken as inputs on the host or on their
        // existing device (for multi-device functions).
        std::unique_ptr<ParallelTensor> parallel_tensor(

  * If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](https://code.google.com/legal/individual-cla-v1.0.html).
  * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](https://code.google.com/legal/corporate-cla-v1.0.html).

+#define UT_NAMESIZE	32
+#define UT_HOSTSIZE	256
 
 
+/* The structure describing an entry in the database of
+   previous logins.  */
 struct lastlog
   {
     time_t ll_time;
@@ -36,12 +38,16 @@ struct lastlog
     char ll_host[UT_HOSTSIZE];
   };
 
+/* The structure describing an entry in the user accounting database.  */
 struct utmp
   {
-    char ut_line[UT_LINESIZE];
-    char ut_user[UT_NAMESIZE];

      } else {
        in_grads.push_back(nullptr);
      }
    } else {
      in_grads.push_back(current_grad->second);
    }
  }

  // Avoid infinite recursion. Whichever forward function we run, it'll end up
  // executing ops, and we don't want to watch those with this accumulator.
  call_state_.emplace(nullptr, true);
  auto pop_call_state = gtl::MakeCleanup([this] { this->call_state_.pop(); });