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);

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:

if [[ "$(ls *.whl | wc -l | tr -d ' ')" != "1" ]]; then
  echo "More than one wheel file is present: moving the oldest to"
  echo "$TFCI_OUTPUT_DIR/extra_wheels."
  # List all .whl files by their modification time (ls -t) and move anything
  # other than the most recently-modified one (the newest one).
  mkdir -p $TFCI_OUTPUT_DIR/extra_wheels
  ls -t *.whl | tail -n +2 | xargs mv -t $TFCI_OUTPUT_DIR/extra_wheels
fi

#include "tensorflow/c/eager/c_api_experimental.h"

#include <cstdint>
#include <memory>
#include <utility>
#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/strings/match.h"
#include "absl/time/time.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api_internal.h"
#include "tensorflow/c/eager/tfe_context_internal.h"
#include "tensorflow/c/eager/tfe_op_internal.h"

      the inputs received by the layer and the eviction policy. When this layer
      is used with an `UpdateEmbeddingCallback`, which is a time-based callback,
      the vocabulary lookup tensor is updated at the time interval set in the
      `UpdateEmbeddingCallback` based on the most up-to-date vocabulary hash
      table maintained by the layer. If this layer is not used in conjunction

        "@com_google_absl//absl/container:flat_hash_map",
        "@com_google_absl//absl/memory",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/time",
        "@local_xla//xla/tsl/c:tsl_status_internal",
        "@local_xla//xla/tsl/distributed_runtime/coordination:coordination_service_agent",
    ],
    alwayslink = 1,
)

cc_library(

  struct stat sbuf;
  if (stat(path, &sbuf) != 0) {
    TF_SetStatusFromIOError(status, errno, path);
  } else {
    stats->length = sbuf.st_size;
    stats->mtime_nsec = sbuf.st_mtime * (1000 * 1000 * 1000);
    stats->is_directory = S_ISDIR(sbuf.st_mode);
    TF_SetStatus(status, TF_OK, "");
  }
}

static int GetChildren(const TF_Filesystem* filesystem, const char* path,

// function.
//
// If `library_filename` has already been loaded, we return a cached handle.
// Device and Kernels/Ops are registered as globals when a library is loaded
// for the first time.
TF_Library* TF_LoadPluggableDeviceLibrary(const char* library_filename,
                                          TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)

        ' binary GPU code, or as "sm_xy" to only include the binary '
        'code.\nPlease note that each additional compute capability '
        'significantly increases your build time and binary size, and that '
        'TensorFlow only supports compute capabilities >= 3.5 [Default is: '
        '%s]: ' % default_cuda_compute_capabilities)
    tf_cuda_compute_capabilities = get_from_env_or_user_or_default(

      }
    }
  });
  if (!s.ok()) {
    return s;
  }

  std::unordered_map<int64_t, int64_t> gradients_size;
  // TODO(apassos) multiple threads could be dequeuing from op_stack at the same
  // time, for better CPU backprop performance.
  VLOG(1) << "Initial stack:";
  if (VLOG_IS_ON(1)) {
    for (auto t : op_stack) {
      VLOG(1) << "  " << t;
    }
  }
  while (!op_stack.empty()) {