TF_Status* status = TF_NewStatus();
  auto* buckets = TFE_MonitoringNewExponentialBuckets(1.0, 2.0, 2);
  auto* sampler =
      TFE_MonitoringNewSampler0("test/sampler", buckets, status, "test");
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  auto* cell = TFE_MonitoringGetCellSampler0(sampler);
  TFE_MonitoringSamplerCellAdd(cell, 1.0);
  auto* collection_registry = monitoring::CollectionRegistry::Default();

*.pbxproj
*.xcworkspace
/*.podspec
/tensorflow/lite/**/coreml/**/BUILD
/tensorflow/lite/**/ios/BUILD
/tensorflow/lite/**/objc/BUILD
/tensorflow/lite/**/swift/BUILD
/tensorflow/lite/examples/ios/simple/data/*.tflite
/tensorflow/lite/examples/ios/simple/data/*.txt
Podfile.lock
Pods

specific Python version.

##### Parameters
`path_to_tensorflow_wheel`: The local path to the TensorFlow wheel file.
Example: `/tmp/tensorflow-2.14.0-cp311-cp311-linux_x86_64.whl`

`python_version`: The target Python version, replacing `.` with `_`.
Example: For Python 3.11, use `3_11`

The script performs the following steps:

1. Navigates to the `../requirements_updater` directory.

  // Write {0, 1, 2, 3} as tensorflow::Example inputs.
  Tensor input(tensorflow::DT_STRING, TensorShape({4}));
  for (int64_t i = 0; i < input.NumElements(); ++i) {
    tensorflow::Example example;
    auto* feature_map = example.mutable_features()->mutable_feature();
    (*feature_map)["x"].mutable_float_list()->add_value(i);
    input.flat<tstring>()(i) = example.SerializeAsString();
  }

// 6. Create a context D using the full cluster server def E.
// 7. Read the variable in D.
TEST(CAPI, SingleHostServerDefV1Works) {
  // Create a server def that represents a 2-process cluster and a client.
  // Example:
  //
  // cluster { job { name: "worker"
  //                 tasks { key: 0 value: "localhost:14522" }
  //                 tasks { key: 1 value: "localhost:14523" }
  //               }

    *   Added a new `rerandomize_each_iteration` argument for the `tf.data.Dataset.sample_from_datasets()` operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. If `seed` is set and `rerandomize_each_iteration=True`, the `sample_from_datasets()` operation will use a different (deterministic) sequence of numbers every epoch.

*   `tf.test`:

set -euxo pipefail

# Run this from inside the tensorflow github directory.
# Usage: setup_venv_test.sh venv_and_symlink_name "glob pattern for one wheel file"
# Example: setup_venv_test.sh bazel_pip "/tf/pkg/*.whl"
# 
# This will create a venv with that wheel file installed in it, and a symlink
# in ./venv_and_symlink_name/tensorflow to ./tensorflow. We use this for the
# "pip" tests.

                                const char* label1, const char* label2);

// -----------------------------------------------------------------------------
// Monitoring Sampler APIs.
// These APIs de-templated monitoring Sampler for swig.

typedef struct TFE_MonitoringSamplerCell TFE_MonitoringSamplerCell;

// Atomically add the value of the cell.
TF_CAPI_EXPORT extern void TFE_MonitoringSamplerCellAdd(

*   [C/C++ license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op.cc#L1)
*   [Python license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn.py#L1)
*   [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1)

            cat errors.txt
            rm errors.txt
            false
        fi
    done
}

@test "No duplicate files on Windows" {
    cat <<EOF
Please rename files so there are no repeats. For example, README.md and
Readme.md would be the same file on Windows. In this test, you would get a
warning for "readme.md" because it makes everything lowercase. There are
repeats of these filename(s) with different casing:
EOF