// off, and an execution operation for named operations. Often execution will
// simply wrap op execution on one or more physical devices.
//
// device_info is an opaque caller-defined type stored with the custom device
// which is passed to the functions referenced in the TFE_CustomDevice struct
// `device` (execute, delete_device, etc.). It can for example contain the
// names of wrapped devices.
//

*   `tf.data`:

    *   Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the `experimental_symbolic_checkpoint` option of `tf.data.Options()`.

// `GradientTape::Watch`.) The op_id is simply a unique index assigned to each
// op executed under the tape. A separate map (`tensorflow::eager::OpTape`)
// maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`,
// inputs and outputs and the gradient function These data structures combined
// allow us to trace the data dependencies between operations and hence compute
// gradients.
//

//
// Questions left to address:
// * Might at some point need a way for callers to provide their own Env.
// * Maybe add TF_TensorShape that encapsulates dimension info.
//
// Design decisions made:
// * Backing store for tensor memory has an associated deallocation
//   function.  This deallocation function will point to client code
//   for tensors populated by the client.  So the client can do things
//   like shadowing a numpy array.

from the current build or not.

It uses the `_api/v2/api_packages.txt` file from the local wheel file.
The `_api/v2/api_packages.txt` file is created during the process of generating
TensorFlow API v2 init files and is stored in the wheel file after the build.

See README.md file for "how to run" instruction.
"""

import logging
import unittest
import pkg_resources

logging.basicConfig(level=logging.INFO)

// function and deleted (as the backprop code creates lots of gradients the user
// is not interested in).
//
// BackwardFunction needs to be a closure which stores intermediate activations
// from the forward computation and calls a vector-jacobian product function
// (also known as adjoint function) to compute, given downstream gradients,
// upstream gradients.
//

    if [[ ! -s $BATS_TEST_TMPDIR/files ]]; then return 0; fi
    xargs -a $BATS_TEST_TMPDIR/files -n1 -P $(nproc --all) \
        python -m pylint --rcfile=tensorflow/tools/ci_build/pylintrc --score false \
        | grep -v "**** Module" \
        | tee $BATS_TEST_TMPDIR/needs_help.txt
    [[ ! -s $BATS_TEST_TMPDIR/needs_help.txt ]]
}

teardown_file() {
    bazel shutdown

  set +u
  for env_file in ${TFCI//,/ }; do
    source "./ci/official/envs/$env_file"
  done
  set -u
  echo '==TFCI==: Evaluated the following TFCI variables from $TFCI:'
  export -p | grep TFCI

  # Load those stored pre-existing TFCI_ vars, if any
  if [[ -s "$FROM_ENV" ]]; then
    echo '==TFCI==: NOTE: Loading the following env parameters, which were'
    echo 'already set in the shell environment. If you want to disable this'