```
# Copy and paste here
```

**Standalone code to reproduce the issue** 
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.

Also, please include a link to a GraphDef or the model if possible.

**Any other info / logs**

Include any logs or source code that would be helpful to diagnose the problem.

    id: code-to-reproduce
    attributes:
      label: Standalone code to reproduce the issue
      description: Provide a reproducible test case that is the bare minimum necessary to generate the problem. Please share a link to Colab, Jupyter, or any notebook.
      placeholder: Tell us what you see!
      value:
      render: shell
    validations:
      required: true
  - type: textarea
    id: logs
    attributes:

    TAG=$(head -n 1 "$KOKORO_PIPER_DIR/presubmit_request.txt" | cut -d" " -f2)
  else
    TAG="pr-${KOKORO_GITHUB_PULL_REQUEST_NUMBER}"
  fi
fi

# Build for both JAX and TF usage.  We do these in one place because they share
# almost all of the same cache layers
export DOCKER_BUILDKIT=1
for target in jax tf; do
  IMAGE="gcr.io/tensorflow-sigs/build-arm64:$target-$TAG"
  docker pull "$IMAGE" || true

      r._elem.remove(testsuite._elem)
  if len(r) > 0:  # pylint: disable=g-explicit-length-test
    result += r

# Insert the number of failures for each test to help identify flakes
# need to clarify for shard
for p in result._elem.xpath(".//error | .//failure"):
  key = re.sub(r"0x\w+", "", p.getparent().get("name", "")) + p.text
  p.text = runfiles_matcher.sub("[testroot]/", p.text)

       That setting must exist for this script to function correctly.
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.

    reproduce the issue description: Provide a reproducible test case that is
    the bare minimum necessary to generate the problem. If possible, please
    share a link to Colab/Jupyter/any notebook. placeholder: Tell us what you
    see! value: render: shell validations: required: true

-   type: textarea id: logs attributes: label: Relevant log output description:

build:rbe_linux --linkopt=-lrt
build:rbe_linux --host_linkopt=-lrt
build:rbe_linux --linkopt=-lm
build:rbe_linux --host_linkopt=-lm

build:rbe_linux_cpu --config=rbe_linux
# Linux cpu and cuda builds share the same toolchain now.
build:rbe_linux_cpu --host_crosstool_top="@sigbuild-r2.17-clang_config_cuda//crosstool:toolchain"
build:rbe_linux_cpu --crosstool_top="@sigbuild-r2.17-clang_config_cuda//crosstool:toolchain"

  (`Settings` > `Apps` > `Google Play Services` > `App details`):

**Standalone code to reproduce the issue**
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to or attach code demonstrating
the problem.

**Any other info / logs**
Include any logs or source code that would be helpful to diagnose the problem.

# export TFCI=py311,linux_x86,no_docker

# Advanced: Use Remote Build Execution (RBE) (internal developers only)
#
#   RBE dramatically speeds up builds and testing. It also gives you a
#   public URL to share your build results with collaborators. However,
#   it is only available to a limited set of internal TensorFlow developers.
#
#   RBE is incompatible with local caching, so you must remove

      shards to disk. `tf.train.experimental.ShardByTaskPolicy` is the default
      sharding behavior, but `tf.train.experimental.MaxShardSizePolicy` can be
      used to shard the checkpoint with a maximum shard file size. Users with
      advanced use cases can also write their own custom
      `tf.train.experimental.ShardingCallback`s.

* `tf.train.CheckpointOptions`