#   and scripts on Linux create a persistent container called "tf"
#   which mounts your TensorFlow directory into the container.
#
#   Important: because the container is persistent, you cannot change TFCI
#   variables in between script executions. To forcibly remove the
#   container and start fresh, run "docker rm -f tf". Removing the container
#   destroys some temporary bazel data and causes longer builds.
#

EOF
  exit 1
fi
TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX"

# These flags share the user's gcloud credentials with the container, so that bazel
# inside the container can authenticate. Note: TF's CI does not have any credential
# stored here.

/.tf_configure.bazelrc
/xla_configure.bazelrc
/bazel-*
/bazel_pip
/tools/python_bin_path.sh
/tensorflow/tools/git/gen
/pip_test
/_python_build
*.pyc
__pycache__
*.swp
.vscode/
cmake_build/
tensorflow/contrib/cmake/_build/
.idea/**
/build/
[Bb]uild/
/build_output/
/tensorflow/core/util/version_info.cc
/tensorflow/python/framework/fast_tensor_util.cpp
/tensorflow/lite/gen/**
/tensorflow/lite/tools/make/downloads/**

    *   Deprecate `tf.contrib.learn`. Please check contrib/learn/README.md for
        instructions on how to convert existing code.
    *   `tf.contrib.data`
    *   Remove deprecated `tf.contrib.data.Dataset`, `tf.contrib.data.Iterator`,
        `tf.contrib.data.FixedLengthRecordDataset`,
        `tf.contrib.data.TextLineDataset`, and `tf.contrib.data.TFRecordDataset`
        classes.

# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then
# gathers the list of all packages (i.e. directories) which contain those
# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \
  | grep -e "^//" -e "^@" \
  | grep -E -v "^//tensorflow" \
  | sed -e 's|:.*||' \
  | sort -u
}

### Typical Pull Request Workflow -

**1. New PR**

- As a contributor, you submit a New PR on GitHub.
- We inspect every incoming PR and add certain labels to the PR such as `size:`,
  `comp:` etc.  At this stage we check if the PR is valid and meets certain
  quality requirements. For example, we check if the CLA is signed, PR has
  sufficient description, if applicable unit tests are added, if it is a

        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/kernels:ops_testutil",
        "@com_google_absl//absl/container:inlined_vector",
        "@com_google_absl//absl/strings:str_format",
        "@eigen_archive//:eigen3",
    ],
)

tf_cc_test(
    name = "ops_test",
    size = "medium",

            "//tensorflow/core/common_runtime/eager:tensor_handle",
            "//tensorflow/core/profiler/lib:traceme",
            "@com_google_absl//absl/algorithm:container",
            "@com_google_absl//absl/types:span",
            "@com_google_absl//absl/types:variant",
        ],
    }) + [
        ":abstract_tensor_handle",
        "//tensorflow/c:c_api_macros_hdrs",

    srcs_version = "PY3",
    visibility = ["//visibility:public"],
    deps = [
        ":tensorflow_py_no_contrib",
        "//tensorflow/lite/python:analyzer",
        "//tensorflow/lite/python:lite",
        "//tensorflow/lite/python/authoring",
    ],
)

py_library(
    name = "tensorflow_py_no_contrib",
    srcs = select({
        "api_version_2": [":tf_python_api_gen_v2"],

        "//tensorflow/core/platform:status",
        "@com_google_absl//absl/log",
    ],
    alwayslink = 1,
)

# Library implementing helper functionality, so that the above only contains
# the API implementation for modular filesystems.
cc_library(
    name = "posix_filesystem_helper",
    srcs = ["posix_filesystem_helper.cc"],
    hdrs = ["posix_filesystem_helper.h"],
    deps = [":copy_file"],
)