ops_->stat(filesystem_.get(), translated_name.c_str(), &stats,
             plugin_status.get());

  if (TF_GetCode(plugin_status.get()) == TF_OK) {
    stat->length = stats.length;
    stat->mtime_nsec = stats.mtime_nsec;
    stat->is_directory = stats.is_directory;
  }

  return StatusFromTF_Status(plugin_status.get());
}

Status ModularFileSystem::IsDirectory(const std::string& name,

  ::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;
    stats->is_directory = cc_stats.is_directory;
  }
}

void TF_NewWritableFile(const char* filename, TF_WritableFileHandle** handle,
                        TF_Status* status) {

    TF_StringStreamDone(children);

    TF_FileStatistics stats;
    TF_FileStat(filepath.c_str(), &stats, s);
    ASSERT_EQ(stats.length, strlen(data));
    ASSERT_FALSE(stats.is_directory);
    ASSERT_GT(stats.mtime_nsec, 0);

    // Trying to delete a non-empty directory should fail.
    TF_DeleteDir(dirpath.c_str(), s);
    ASSERT_NE(TF_OK, TF_GetCode(s))
        << "TF_DeleteDir unexpectedly succeeded with a non-empty directory "

  const char* path;

  if (!TF_StringStreamNext(s, &path)) {
    fprintf(stderr, "TF_GetLocalTempDirectories returned no results\n");
    return 1;
  }

  char file_name[100];
  time_t t = time(NULL);
  snprintf(file_name, sizeof(file_name), "test-%d-%ld.txt", getpid(), t);

  size_t length = 2 + strlen(path) + strlen(file_name);
  char* full_path = malloc(length);

If you are experiencing or witnessing conflict, we ask you to use the following escalation strategy to address the conflict:

1.  Address the perceived conflict directly with those involved, preferably in a
    real-time medium.
2.  If this fails, get a third party (e.g. a mutual friend, and/or someone with
    background on the issue, but not involved in the conflict) to intercede.

setattr(_current_module, "optimizers", _optimizers)
setattr(_current_module, "initializers", _initializers)


# Do an eager load for Keras' code so that any function/method that needs to
# happen at load time will trigger, eg registration of optimizers in the
# SavedModel registry.
# See b/196254385 for more details.
try:
  if _tf_uses_legacy_keras:
    importlib.import_module("tf_keras.src.optimizers")
  else:

// The number of dimensions used to represent the tensor on device can be
// different from the number returned by TFE_TensorHandleNumDims.
// The return value was current at the time of TFE_TensorDebugInfo creation.
TF_CAPI_EXPORT extern int TFE_TensorDebugInfoOnDeviceNumDims(
    TFE_TensorDebugInfo* debug_info);

// Returns the number of elements in dimension `dim_index`.

-	b	2f				\n\
-1:	.word	_dl_start			\n\
-2:						\n\
-       " : "=r" (static_addr),  "=r" (dynamic_addr));
-  return dynamic_addr - static_addr;
+    _DYNAMIC sysmbol is used here as its link-time address stored in
+    the special unrelocated first GOT entry.  */
+
+    extern ElfW(Dyn) _DYNAMIC[] attribute_hidden;
+    return (ElfW(Addr)) &_DYNAMIC - elf_machine_dynamic ();
 }
 

  stat->base.length = metadata->size();
  stat->base.mtime_nsec =
      metadata->time_storage_class_updated().time_since_epoch().count();
  stat->base.is_directory = object.back() == '/';
  TF_VLog(1,
          "Stat of: gs://%s/%s --  length: %u generation: %u; mtime_nsec: %u;",
          bucket.c_str(), object.c_str(), stat->base.length,
          stat->generation_number, stat->base.mtime_nsec);
  return TF_SetStatus(status, TF_OK, "");
}

// that provides the filesystem functionality. However, the POSIX filesystem
// needs to be statically registered in some tests and utilities for building
// the API files at the time of creating the pip package. Hence, we need to
// expose this function so that this filesystem can be statically registered
// when needed.
void TF_InitPlugin(TF_FilesystemPluginInfo* info);