from the `envs` directory that is filled with configuration options:

-   Nightly jobs (Run nightly on the `nightly` branch)
    -   Uses `wheel.sh`, `libtensorflow.sh`, `code_check_full.sh`
-   Continuous jobs (Run on every GitHub commit)
    -   Uses `pycpp.sh`
-   Presubmit jobs (Run on every GitHub PR)
    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

are working with. When loading untrusted checkpoints, the values of the traced
variables from your model are also going to be untrusted. That means that if
your code interacts with the filesystem, network, etc. and uses checkpointed
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

Copyright (c) 2014, The Regents of the University of California (Regents)
All rights reserved.

All other contributions:

Copyright (c) 2014, the respective contributors
All rights reserved.

Caffe uses a shared copyright model: each contributor holds copyright over
their contributions to Caffe. The project versioning records all such
contribution and copyright details. If a contributor wants to further mark

            `nodes_and_registration()` method, both of which were previously
            documented as temporary and to be removed.
            *   Uses of `tensors()` can be replaced by calling the existing
                methods `tensors_size()` and `tensor(int)`.
            *   Uses of the non-const overload of `nodes_and_registration` can
                be replaced by calling the existing methods `nodes_size()` and

            "c_api_unified_experimental_internal.h",
            "graph_function.h",
            "graph_function.cc",
            "gradient_checker.cc",
            "gradient_checker.h",
            "gradients.cc",  # Uses RTTI.
            "tracing_utils.h",
            "tracing_utils.cc",
            "*test*",
            "*dlpack*",
        ],
    ),
    visibility = ["//visibility:public"],

    }),
)

# -------------------------------------------
# New rules should be added above this target.
# -------------------------------------------

# TensorFlow uses several libraries that may also be used by applications
# linking against the C and C++ APIs (such as libjpeg).  When we create
# the shared library, only export the core TF API functions to avoid

title: TensorFlow, Large-scale machine learning on heterogeneous systems

build --experimental_link_static_libraries_once=false

# Prevent regressions on those two incompatible changes
# TODO: remove those flags when they are flipped in the default Bazel version TF uses.
build --incompatible_enforce_config_setting_visibility
# TODO: also enable this flag after fixing the visibility violations
# build --incompatible_config_setting_private_default_visibility