-   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:

-   Different Python versions

typedef struct TF_VariableInfo TF_VariableInfo;

// Returns a `TF_Device` pointer, which actually points to a C++ `Device`.
// Currently we only allow `NextPluggableDevice` to be casted as `TF_Device`,
// but in theory every this is a C API for every kind of device.
TF_CAPI_EXPORT extern TF_Device* TF_GetDevice(TF_OpKernelContext* ctx);

// --------------------------  Resource  ---------------------------------------

### 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

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
      # helpful since the only examples I've seen are enormous.

# variables in it, OR may be left empty if the user has already exported the
# relevant variables in their environment. Because of 'set -o allexport' above
# (which is equivalent to "set -a"), every variable in the file is exported
# for other files to use.
#
# Separately, if TFCI is set *and* there are also additional TFCI_ variables
# set in the shell environment, those variables will be restored after the

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
      # helpful since the only examples I've seen are enormous.

# ==============================================================================
# Note: this file gets sourced in utilities/setup.sh, which has "set -u"
# (error on undefined variables). This ensures that (a) every TFCI variable
# has an explicit default value, and (b) no script can accidentally use a
# variable that doesn't exist. Please keep this list in alphabetical order.
#
# Find usage in scripts with e.g.:
#   cd ci/official

# This bazelrc can build a CPU-supporting TF package.

# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

#ifdef __cplusplus
extern "C" {
#endif

// Resets `op_to_reset` with `op_or_function_name` and `raw_device_name`. This
// is for performance optimization by reusing an exiting unused op rather than
// creating a new op every time. If `raw_device_name` is `NULL` or empty, it
// does not set the device name. If it's not `NULL`, then it attempts to parse
// and set the device name. It's effectively `TFE_OpSetDevice`, but it is faster

      : status_(TF_NewStatus()),
        // If the context's default exector is set to async, re-using that in
        // each thread would cause collectives to deadlock. For consistency we
        // create a new sync executor for every thread.
        //
        // TODO(allenl): We should have an async API that works with the
        // parallel device.
        device_(device),
        executor_(