`"LOCAL"` (case insensitive). If `"AUTO"`, tf.data service runtime
        decides which workers to read from. If `"ANY"`, TF workers read from any
        tf.data service workers. If `"LOCAL"`, TF workers will only read from
        local in-processs tf.data service workers. `"AUTO"` works well for most
        cases, while users can specify other targets. For example, `"LOCAL"`

# Contributing guidelines

## Pull Request Checklist

Before sending your pull requests, make sure you do the following:

-   Read the [contributing guidelines](CONTRIBUTING.md).
-   Read the [Code of Conduct](CODE_OF_CONDUCT.md).
-   Ensure you have signed the
    [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
-   Check if your changes are consistent with the

  const uint64_t max_age_;

  /// The maximum number of entries in the cache. A value of 0 means there is no
  /// limit on entry count.
  const size_t max_entries_;

  /// The callback to read timestamps.
  std::function<uint64_t()> timer_seconds_;

  /// Guards access to the cache and the LRU list.
  absl::Mutex mu_;

  /// The cache (a map from string key to Entry).

  executed = false;
  num_retvals = 0;
  TFE_Execute(op.get(), nullptr, &num_retvals, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  ASSERT_TRUE(executed);

  // Read the variable's value.
  op.reset(TFE_NewOp(context.get(), "ReadVariableOp", status.get()));
  TFE_OpAddInput(op.get(), var_handle, status.get());
  TFE_OpSetDevice(op.get(), name, status.get());

  }

  // Read from the variable and verify that we have a parallel tensor.
  {
    TensorHandlePtr read = variable->Read(context, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
    std::array<TensorHandlePtr, 2> components;
    ExtractPerDeviceValues(context, read.get(), &components, status.get());

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Sourcing this enables Bazel remote cache (public, read-only)
# The cache configs are different for MacOS and Linux
if [[ $(uname -s) == "Darwin" ]]; then
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache"
else

# limitations under the License.
# ==============================================================================

name: cffconvert

on:
  push:
    paths:
      - CITATION.cff

permissions:
  contents: read

jobs:
  validate:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    name: "validate"
    runs-on: ubuntu-latest
    steps:
      - name: Check out a copy of the repository

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/devel.usertools/code_check_full.bats
Here are the affected tests:
EOF
    while read dep; do
      echo "For dependency $dep:"
      # For every missing dependency, find the tests which directly depend on
      # it, and print that list for debugging. Not really clear if this is

void Close(const TF_WritableFile* file, TF_Status* status);
}  // namespace tf_writable_file

namespace tf_read_only_memory_region {
void Cleanup(TF_ReadOnlyMemoryRegion* region);
const void* Data(const TF_ReadOnlyMemoryRegion* region);
uint64_t Length(const TF_ReadOnlyMemoryRegion* region);
}  // namespace tf_read_only_memory_region

namespace tf_gcs_filesystem {
typedef struct GcsFileStat {
  TF_FileStatistics base;

variables as part of those interactions (ex: using a string variable to build a
filesystem path), a maliciously created checkpoint might be able to change the
targets of those operations, which could result in arbitrary
read/write/executions.

### Running a TensorFlow server

TensorFlow is a platform for distributed computing, and as such there is a
TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for