TF_SetStatus(status, TF_OK, "");
}

static void NewWritableFile(const TF_Filesystem* filesystem, const char* path,
                            TF_WritableFile* file, TF_Status* status) {
  FILE* f = fopen(path, "w");
  if (f == nullptr) {
    TF_SetStatusFromIOError(status, errno, path);
    return;
  }

  file->plugin_file = new tf_writable_file::PosixFile({strdup(path), f});
  TF_SetStatus(status, TF_OK, "");

selects.config_setting_group(
    name = "is_cuda_enabled_and_windows",
    match_all = [
        ":is_cuda_enabled",
        ":windows",
    ],
)

# Config setting to use in select()s to distinguish open source build from
# google internal build on configurable attributes.
#
# For non-configurable distinction between OSS and Google builds, see
# `if_oss()` and `if_google()` macros in tensorflow.bzl.
config_setting(

  delete gcs_file;
}

void Append(const TF_WritableFile* file, const char* buffer, size_t n,
            TF_Status* status) {
  auto gcs_file = static_cast<GCSFile*>(file->plugin_file);
  if (!gcs_file->outfile.is_open()) {
    TF_SetStatus(status, TF_FAILED_PRECONDITION,
                 "The internal temporary file is not writable.");
    return;
  }
  TF_VLog(3, "Append: gs://%s/%s size %u", gcs_file->bucket.c_str(),

        -  It could also be a simple python or TF utility that you could easily copy over to your own codebase. In those case, just make it your own!
        -  If you believe it should definitely be a public Keras API, please open a feature request in keras GitHub repo.
        -  As a workaround, you could import the same private symbol keras `keras.src`, but keep in mind the `src` namespace is not stable and those APIs may change or be removed in the future.

          labels-to-remove-when-unstale: 'stat:awaiting response'
          only-labels: "stat:awaiting response"
          stale-issue-message: > 
            This issue is stale because it has been open for 7 days with no activity.
            It will be closed if no further activity occurs. Thank you.
          close-issue-message: >

  # Write tools/python_bin_path.sh
  with open(
      os.path.join(_TF_WORKSPACE_ROOT, 'tools', 'python_bin_path.sh'),
      'w') as f:
    f.write('export PYTHON_BIN_PATH="{}"'.format(python_bin_path))


def reset_tf_configure_bazelrc():
  """Reset file that contains customized config settings."""
  open(_TF_BAZELRC, 'w').close()


def cleanup_makefile():

      api_packages_path = pkg_resources.resource_filename(
          "tensorflow", "_api/v2/api_packages.txt"
      )

      logging.info("Load api packages file: %s", api_packages_path)
      with open(api_packages_path) as file:
        return set(file.read().splitlines())

    super().setUp()
    self.api_packages_v2 = _get_api_packages_v2()
    # Some paths in the api_packages_path file cannot be directly imported.

authors:...

  return parsed_args


def parse_log(file_path: str,
              verbose: bool = False) -> ResultDictType:
  """Finds ResultStore links, and tries to determine their status."""
  with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
    log_lines = f.read().splitlines()

  result_store_links: ResultDictType = {}
  current_url = None
  for i in range(len(log_lines)):
    line = log_lines[i]

**`Documentation`** |
------------------- |
[![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) |

[TensorFlow](https://www.tensorflow.org/) is an end-to-end open source platform
for machine learning. It has a comprehensive, flexible ecosystem of
[tools](https://www.tensorflow.org/resources/tools),
[libraries](https://www.tensorflow.org/resources/libraries-extensions), and