tensorflow::Tensor tensor(allocator.get(), DT_FLOAT, {1});
  TF_ASSERT_OK_AND_ASSIGN(
      auto pjrt_client,
      xla::GetTfrtCpuClient(/*asynchronous=*/true, /*cpu_device_count=*/1));
  std::vector<int32_t> data(1, 0);
  xla::Shape shape = xla::ShapeUtil::MakeShape(xla::S32, {1});
  TF_ASSERT_OK_AND_ASSIGN(
      auto buffer,
      pjrt_client->BufferFromHostBuffer(

  // Assigns a new value to the variable.
  void Assign(TFE_Context* context, TFE_TensorHandle* value, TF_Status* status);
  // Adds `value` to the existing value of the variable.
  void AssignAdd(TFE_Context* context, TFE_TensorHandle* value,
                 TF_Status* status);

 private:
  // Helper for running any single-argument assignment ops (Assign, AssignAdd,
  // AssignSub, ...).

    console.log(pr_resp);
    throw `PR:{pr_number} needs to be valid and closed (merged)`;
  }
  const pr_title = pr_resp.data.title;
  // Assign to PR owner and reviewers
  const assignees = pr_resp.data.assignees.concat(pr_resp.data.requested_reviewers);
  let assignee_logins = assignees.map(x => x.login);
  assignee_logins.push(pr_resp.data.user.login);

reachable and exploitable through production-grade, benign models.

### Compilation

Compiling models via the recommended entry points described in
[XLA](https://www.tensorflow.org/xla) and
[JAX](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)
documentation should be safe, while some of the testing and debugging tools that
come with the compiler are not designed to be used with untrusted data and

dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports...

    throw "Error adding labels to PR";
  }
  if (assignees.length > 0) {
    const resp_assign = await github.rest.issues.addAssignees({
      issue_number: context.issue.number,
      owner: context.repo.owner,
      repo: context.repo.repo,
      assignees: assignees
    });
    if (resp_assign.status >= 400) {
      console.log(resp_assign);
      throw "Error adding assignee to PR";
    }
  }

#
# setup.packages.sh: Given a list of Ubuntu packages, install them and clean up.
# Usage: setup.packages.sh <package_list.txt>
set -e

# Prevent apt install tzinfo from asking our location (assumes UTC)
export DEBIAN_FRONTEND=noninteractive

apt-get update
# Remove commented lines and blank lines
apt-get install -y --no-install-recommends $(sed -e '/^\s*#.*$/d' -e '/^\s*$/d' "$1" | sort -u)

# Usage: setup.python.sh <pyversion> <requirements.txt>

# Sets up custom apt sources for our TF images.

# Prevent apt install tzinfo from asking our location (assumes UTC)
export DEBIAN_FRONTEND=noninteractive

# Set up shared custom sources
apt-get update
apt-get install -y gnupg ca-certificates

# Deadsnakes: https://launchpad.net/~deadsnakes/+archive/ubuntu/ppa

  if (av_tensor == nullptr) {
    TF_ASSIGN_OR_RETURN(
        *tensor, MakeTensorFromPjRtBuffer(tensor->dtype(), tensor->shape(),
                                          std::move(buffer)));
  } else {
    av_tensor->SetBuffer(std::move(buffer));
  }
  return absl::OkStatus();
}

absl::StatusOr<xla::PjRtCApiClient*> GetPjRtCApiClient(
    const DeviceType& device_type) {

# ==============================================================================

name: Trusted Partner PR
on:
  pull_request_target:

permissions:
  contents: read

jobs:
  assign-partner-tags:
    runs-on: ubuntu-latest
    permissions:
      # Needed to attach tags into the PR
      issues: write
      contents: write
      pull-requests: write
    if: |