distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/c/experimental/gradients/nn_grad.h"

#include "absl/types/span.h"

// When an error happens, any pending operations are discarded, and newly issued
// ops return an error. This call clears the error state and re-enables
// execution of newly issued ops.
//
// Note that outputs of discarded ops remain in a corrupt state and should not
// be used for future calls.
// TODO(agarwal): mark the affected handles and raise errors if they are used.

# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
source ci/official/envs/linux_arm64

COPY setup.packages.sh setup.packages.sh
COPY builder.packages.txt builder.packages.txt
RUN /setup.packages.sh /builder.packages.txt
RUN update-ca-certificates
# Install devtoolset-9 in /dt10 with glibc 2.17 and libstdc++ 4.8, for building
# manylinux2014-compatible packages.
COPY builder.devtoolset/fixlinks_aarch64.sh /fixlinks.sh
COPY builder.devtoolset/rpm-patch.sh /rpm-patch.sh

// A TF_AbstractTensor is an input to an operation. E.g. it could be a union
// type of eager and graph tensors. It is also the result of executing an
// operation.
typedef struct TF_AbstractTensor TF_AbstractTensor;

// A TF_AbstractOp is the metadata we need to execute an operation. E.g. this
// could contain the op type and other attributes.
typedef struct TF_AbstractOp TF_AbstractOp;

# limitations under the License.
# ==============================================================================

# Usage: Go to
# https://github.com/tensorflow/tensorflow/actions/workflows/release-branch-cherrypick.yml
# and click "Run Workflow." Leave "Use Workflow From" set to "master", then
# input the branch name and paste the cherry-pick commit and click Run. A PR
# will be created.

EOF
  # Find any "Streaming build results to" line, then print the last word in it,
  # and don't print duplicates
  awk '/Streaming build results to/ {print $NF}' "$TFCI_OUTPUT_DIR/script.log" | uniq
}

# Print out any ResultStore URLs for Bazel invocations' results.
# Each failed target there will have its own representation, making failures
# easier to find and read.
function resultstore_extract {
  local \

# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

# script to run pip-compile for each requirement.

distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_
#define TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_

# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

name: PyLint
on:
  pull_request:
    paths:
      - '**.py'

permissions: