absl::Span<AbstractTensorHandle*> grad_inputs) override {
    // TODO(vnvo2409): Add control dependency
    /* Given upstream grad U and a Log1p op: Y = log(1 + X), the gradients are:
     *
     *    dX = U / (1 + X)
     *
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];
    AbstractTensorHandle* X = forward_inputs_[0];

        By default, Keras always writes the logs to stdout. However, this is not
        optimal in a non-interactive environment, where you don't have access to
        stdout, but can only view the logs. You can use
        `tf.keras.utils.disable_interactive_logging()` to write the logs to ABSL
        logging. You can also use `tf.keras.utils.enable_interactive_logging()`

        "//tensorflow/lite/kernels:reference_ops",
        "//tensorflow/lite/schema:schema_fbs",
        "//tensorflow/lite/toco/logging:conversion_log_util",
        "//tensorflow/lite/toco/logging:toco_conversion_log_proto_cc",
        "//tensorflow/lite/toco:model_flags_proto_cc",
        "//tensorflow/lite/toco:toco_convert",
        "//tensorflow/lite/toco:toco_flags_proto_cc",

# Do not risk cache corruption. See:
# https://github.com/bazelbuild/bazel/issues/3360
build:linux --experimental_guard_against_concurrent_changes

# Configure short or long logs
build:short_logs --output_filter=DONT_MATCH_ANYTHING
build:verbose_logs --output_filter=

# Instruction set optimizations
# TODO(gunan): Create a feature in toolchains for avx/avx2 to
#   avoid having to define linux/win separately.

      placeholder: Tell us what you see!
      value:
      render: shell
    validations:
      required: true
  - type: textarea
    id: logs
    attributes:
      label: Relevant log output
      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.

  files = subprocess.check_output(
      ["grep", "-rlE", '(failures|errors)="[1-9]', sys.argv[1]]
  )
except subprocess.CalledProcessError as e:
  print("No failures found to log!")
  exit(0)

# For test cases, only show the ones that failed that have text (a log)
seen = collections.Counter()
runfiles_matcher = re.compile(r"(/.*\.runfiles/)")


for f in files.strip().splitlines():
  # Just ignore any failures, they're probably not important

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.
If including tracebacks, please include the full traceback. Large logs and files

      else:
        tests_failed = re.search(TESTS_FAILED_RE, backtrack_line)
      if build_failed or tests_failed:
        log_fragment = '\n'.join(
            log_lines[max(k - 20, 0):min(end_line + 1, len(log_lines) - 1)])
        lines['log_fragment'] = log_fragment
        lines['status'] = (InvokeStatus.build_failed if build_failed
                           else InvokeStatus.tests_failed)
        if verbose:

  if (pr_match_groups.length != 2) {
    console.log(`PR Number not found in ${context.payload.head_commit.message}`);
    throw "Error extracting PR Number from commit message";
  }
  const pr_number = parseInt(pr_match_groups[1]);
  const owner = context.payload.repository.owner.name;
  const repo = context.payload.repository.name;
  console.log(`Original PR: ${pr_number} and Rollback Commit: ${rollback_commit}`);

export TFCI_OUTPUT_DIR=$(realpath "$TFCI_OUTPUT_DIR")
mkdir -p "$TFCI_OUTPUT_DIR"

# In addition to dumping all script output to the terminal, place it into
# $TFCI_OUTPUT_DIR/script.log
exec > >(tee "$TFCI_OUTPUT_DIR/script.log") 2>&1

# Setup tfrun, a helper function for executing steps that can either be run
# locally or run under Docker. setup_docker.sh, below, redefines it as "docker
# exec".