[Environment]::SetEnvironmentVariable(\"JAVA_HOME\", $zulu_root, \"Machine\")

# Install gcloud (install.bat installs directly into bin folder of extracted zip contents)
# Install needed gcloud components
RUN Add-Type -AssemblyName "System.IO.Compression.FileSystem"; \
    $pkg_url = \"https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-396.0.0-windows-x86_64.zip\"; \

*   [Bash license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/ci_build/ci_build.sh#L2)
*   [JavaScript/TypeScript license example](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/components/tf_backend/backend.ts#L1)

Bazel BUILD files also need to include a license section, e.g.,
[BUILD example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/BUILD#L61).

#### C++ coding style

*   Introduce the `tf.compat.v1.keras.utils.track_tf1_style_variables`
    decorator, which enables using large classes of tf1-style variable_scope,
    `get_variable`, and `compat.v1.layer`-based components from within TF2
    models running with TF2 behavior enabled.

*   `tf.data`:

    *   tf.data service now supports auto-sharding. Users specify the sharding

    std::array<TensorHandlePtr, 2> out_components;
    ExtractPerDeviceValues(context.get(), multiply_result.get(),
                           &out_components, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

    ExpectScalarEq<float>(out_components[0].get(), 9.);
    ExpectScalarEq<float>(out_components[1].get(), 4.);
  }

  worker_server1.release();

  components[0] = negative_one_cpu.get();
  components[1] = negative_one_cpu.get();
  TensorHandlePtr first_negative_one = CreatePerDeviceValues(
      context.get(), components, first_device_name, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  components[0] = first_negative_one.get();
  components[1] = negative_one_cpu.get();

    std::vector<TensorHandlePtr> components;
    components.reserve(underlying_devices_.size());
    for (int j = 0; j < underlying_devices_.size(); ++j) {
      components.push_back(std::move(per_device_output_tensors[j][i]));
    }
    if (expected_output_shapes[i].IsFullyDefined()) {
      per_device_outputs.push_back(ParallelTensor::FromTensorHandles(
          *this, std::move(components),

    std::array<TensorHandlePtr, 2> components;
    ExtractPerDeviceValues(context, read.get(), &components, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

    ExpectScalarEq<float>(components[0].get(), 20.);
    ExpectScalarEq<float>(components[1].get(), 20.);

    std::string first_device =
        TFE_TensorHandleBackingDeviceName(components[0].get(), status.get());

  // devices of a ParallelDevice. If called, ParallelTensor::Shape inspects
  // `components` to determine a shape.
  static std::unique_ptr<ParallelTensor> FromTensorHandles(
      const ParallelDevice& parallel_device,
      std::vector<TensorHandlePtr> components, TF_Status* status);
  // Uses the provided shape without additional checks, which avoids blocking

    std::array<TensorHandlePtr, num_replicas>* components, TF_Status* status);

// Helper to pack `num_replicas` TFE_TensorHandles into one parallel handle.
template <std::size_t num_replicas>
TensorHandlePtr CreatePerDeviceValues(
    TFE_Context* context,
    const std::array<TFE_TensorHandle*, num_replicas>& components,
    const char* device, TF_Status* status);

namespace tensorflow {
namespace parallel_device {

// Allocate a parallel device named `device_name` which forwards operations to
// `underlying_devices`, maintaining "parallel tensors" with components placed
// on each underlying device.
//
// For example if `device_name` is
//   "/job:localhost/replica:0/task:0/device:CUSTOM:0"
// and `underlying_devices` is
//   {"/job:localhost/replica:0/task:0/device:GPU:0",