# buildifier: disable=load-on-top

workspace(name = "requirements_updater")

# buildifier: disable=load-on-top

load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")

http_archive(
    name = "bazel_skylib",
    sha256 = "74d544d96f4a5bb630d465ca8bbcfe231e3594e5aae57e1edbf17a6eb3ca2506",
    urls = [

```

To specify the version via a Bazel command argument, use the following:

```
--repo_env=TF_PYTHON_VERSION=3.11
```

## Requirements updater

Requirements updater is a standalone tool, intended to simplify process of
updating requirements for multiple minor versions of Python.

It takes in a file with a set of dependencies, and produces a more detailed

and a requirements_lock file. The requirements_lock file is generated by the
[requirements_updater](https://github.com/tensorflow/tensorflow/tree/master/ci/official/requirements_updater)
tool using the path to this wheel file.

### Hermetic Python

For details about hermetic Python and setting its toolchain version, see

1. Creating a PR and observing the presubmit test results
2. Running the CI scripts locally, as explained below
3. **Google employees only**: Google employees can use an internal-only tool
called "MLCI" that makes testing more convenient: it can execute any full CI job
against a pending change. Search for "MLCI" internally to find it.

You may invoke a CI script of your choice by following these instructions:

```bash
cd tensorflow-git-dir

  LICENSES_TARGET="$2"

  # grep patterns for targets which are allowed to be missing from the licenses
  cat > $BATS_TEST_TMPDIR/allowed_to_be_missing <<EOF
@absl_py//absl
@bazel_tools//platforms
@bazel_tools//third_party/
@bazel_tools//tools
@local
@com_google_absl//absl
@org_tensorflow//
@com_github_googlecloudplatform_google_cloud_cpp//google
@com_github_grpc_grpc//src/compiler
@platforms//os
@ruy//
EOF

// be included in the output properties.
TF_CAPI_EXPORT extern void TF_InferStatically(
    TF_GraphProperties* graph_properties, TF_Bool assume_valid_feeds,
    TF_Bool aggressive_shape_inference, TF_Bool include_input_tensor_values,
    TF_Bool include_output_tensor_values, TF_Status* s);

// Get the size of input OpInfo::TensorProperties given node name.
TF_CAPI_EXPORT extern void TF_GetInputPropertiesListSize(

# For building libtensorflow archives
test:libtensorflow_test -- //tensorflow/tools/lib_package:libtensorflow_test //tensorflow/tools/lib_package:libtensorflow_java_test

your changes by running the command:

```bash
tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh
```

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

#### Running unit tests

There are two ways to run TensorFlow unit tests.

1.  Using tools and libraries installed directly on your system.

    Refer to the

};

struct TFE_MonitoringBoolGauge0 : TFE_MonitoringGauge<bool, 0> {
  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};
struct TFE_MonitoringBoolGauge1 : TFE_MonitoringGauge<bool, 1> {
  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};
struct TFE_MonitoringBoolGauge2 : TFE_MonitoringGauge<bool, 2> {
  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};

struct TFE_MonitoringBuckets {

  // `keep_alive_secs` of inactivity.
  virtual Status SetOrUpdateServerDef(const ServerDef& server_def,
                                      bool reset_context, int keep_alive_secs,
                                      int64_t init_timeout_in_ms, int retries,
                                      bool clear_existing_contexts = false) = 0;

  // Initializes context for the local worker and no contexts will be created