vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.
Eager mode lets users write imperative-style statements that can be easily
inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference

* Edit the text of the PR title to start in "imperative", like giving an order. So, instead of `Adding support for teleporting` use `Add support for teleporting`.

}

// xlMetaV2Version describes the journal entry, Type defines
// the current journal entry type other types might be nil based
// on what Type field carries, it is imperative for the caller
// to verify which journal type first before accessing rest of the fields.
type xlMetaV2Version struct {
	Type             VersionType           `json:"Type" msg:"Type"`

cd "$(dirname "$0")"

if is_continuous_or_release || [[ -z "$KOKORO_BUILD_ID" ]]; then
  # A continuous job is the only one to publish to latest
  TAG="latest-multi-python"
else
  # If it is a change, grab a good tag for iterative builds
  if [[ -z "${KOKORO_GITHUB_PULL_REQUEST_NUMBER}" ]]; then
    TAG=$(head -n 1 "$KOKORO_PIPER_DIR/presubmit_request.txt" | cut -d" " -f2)
  else
    TAG="pr-${KOKORO_GITHUB_PULL_REQUEST_NUMBER}"
  fi
fi

    *   Only create a GCS directory object if the object does not already exist.
    *   Introduce `dynamic` constructor argument in Layer and Model, which
        should be set to `True` when using imperative control flow in the `call`
        method.
    *   Begin adding Go wrapper for C Eager API.
    *   XLA HLO graphs can be inspected with interactive_graphviz tool now.