close-issue-reason: completed
          # List of labels to remove when issues/PRs unstale. 
          labels-to-remove-when-unstale: "stat:contribution welcome,stat:good first issue"
          any-of-labels: "stat:contribution welcome,stat:good first issue"
          stale-issue-message: > 
            This issue is stale because it has been open for 180 days with no activity.

  std::unique_ptr<ExpiringLRUCache<GcsFileStat>> stat_cache;
  GCSFile(google::cloud::storage::Client&& gcs_client);
  // This constructor is used for testing purpose only.
  GCSFile(google::cloud::storage::Client&& gcs_client, bool compose,
          uint64_t block_size, size_t max_bytes, uint64_t max_staleness,
          uint64_t stat_cache_max_age, size_t stat_cache_max_entries);
} GCSFile;

#ifndef TENSORFLOW_C_EXPERIMENTAL_FILESYSTEM_PLUGINS_POSIX_POSIX_FILESYSTEM_HELPER_H_
#define TENSORFLOW_C_EXPERIMENTAL_FILESYSTEM_PLUGINS_POSIX_POSIX_FILESYSTEM_HELPER_H_

#include <dirent.h>
#include <sys/stat.h>

namespace tf_posix_filesystem {

// Copies up to `size` of `src` to `dst`, creating destination if needed.
//
// Callers should pass size of `src` in `size` and the permissions of `src` in

                              TransactionToken* token) override;
  Status CreateDir(const std::string& dirname,
                   TransactionToken* token) override;
  Status Stat(const std::string& fname, TransactionToken* token,
              FileStatistics* stat) override;
  Status IsDirectory(const std::string& fname,
                     TransactionToken* token) override;
  Status GetFileSize(const std::string& fname, TransactionToken* token,

void TF_FileStat(const char* filename, TF_FileStatistics* stats,
                 TF_Status* status) {
  ::tensorflow::FileStatistics cc_stats;
  TF_SetStatus(status, TF_OK, "");
  ::tensorflow::Status s =
      ::tensorflow::Env::Default()->Stat(filename, &cc_stats);
  ::tensorflow::Set_TF_Status_from_Status(status, s);
  if (s.ok()) {
    stats->length = cc_stats.length;
    stats->mtime_nsec = cc_stats.mtime_nsec;

    if (max_age_ == 0) {
      return compute_func(key, value, status);
    }

    // Note: we hold onto mu_ for the rest of this function. In practice, this
    // is okay, as stat requests are typically fast, and concurrent requests are
    // often for the same file. Future work can split this up into one lock per
    // key if this proves to be a significant performance bottleneck.

# Mac version of Core utilities differ in usage. Since our scripts are written
# with the GNU style, we need to set GNU utilities to be default on Mac.
if [[ -n "$(which grealpath)" ]] &&  [[ -n "$(which gstat)" ]]; then
  alias realpath=grealpath
  alias stat=gstat
  # By default, aliases are only expanded in interactive shells, which means
  # that they are not substituted for their corresponding commands in shell

==============================================================================*/
#ifndef TENSORFLOW_C_EXPERIMENTAL_FILESYSTEM_PLUGINS_POSIX_COPY_FILE_H_
#define TENSORFLOW_C_EXPERIMENTAL_FILESYSTEM_PLUGINS_POSIX_COPY_FILE_H_

#include <sys/stat.h>

namespace tf_posix_filesystem {

// Transfers up to `size` bytes from `dst_fd` to `src_fd`.
//
// This method uses `sendfile` if available (i.e., linux 2.6.33 or later) or an
// intermediate buffer if not.
//

    grep --quiet -zoP 'is consistent with the following platform tag:\n"manylinux_2_17_(aarch|x86_)64"\.' audit.txt
}

@test "Wheel conforms to upstream size limitations" {
    WHEEL_MEGABYTES=$(stat --format %s "$TF_WHEEL" | awk '{print int($1/(1024*1024))}')
    # Googlers: search for "test_tf_whl_size"
    case "$TF_WHEEL" in
        # CPU:
        *cpu*manylinux*) LARGEST_OK_SIZE=220 ;;
        # GPU:

abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing...