srcs = ["modular_filesystem_test.cc"],
    linkopts = ["-ldl"],
    tags = [
        "manual",  # Requires DSOs as arguments, eventual setup
        "notap",  # b/139060984, requires implementing modular support for Google filesystem
    ],
    deps = [
        ":modular_filesystem",
        "//tensorflow/core:framework_internal",
        "//tensorflow/core/lib/io:path",
        "//tensorflow/core/platform:env",

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

You may check how your changes will affect TensorFlow by:

1. Creating a PR and observing the presubmit test results

        write_to_bazelrc(
            'build --copt="-DEIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH=1"')

  with_xla_support = environ_cp.get('TF_ENABLE_XLA', None)
  if with_xla_support is not None:
    write_to_bazelrc('build --define=with_xla_support=%s' %
                     ('true' if int(with_xla_support) else 'false'))

  set_action_env_var(
      environ_cp, 'TF_NEED_ROCM', 'ROCm', False, bazel_config_name='rocm')

  virtual Status SetInput(size_t index,
                          ImmediateExecutionTensorHandle* input) = 0;

  virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.
  // Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

    GTEST_SKIP() << "NewWritableFile() not supported: " << status;

  const std::string test_data("asdf");
  status = file->Append(test_data);
  if (!status.ok()) GTEST_SKIP() << "Append() not supported: " << status;
  status = file->Flush();
  if (!status.ok()) GTEST_SKIP() << "Flush() not supported: " << status;
  status = file->Close();
  if (!status.ok()) GTEST_SKIP() << "Close() not supported: " << status;

}

// Validates the read only memory region operations given by the plugin.
static Status ValidateHelper(const TF_ReadOnlyMemoryRegionOps* ops) {
  if (ops == nullptr) {
    // read only memory region support is always optional
    return OkStatus();
  }

  if (ops->cleanup == nullptr)
    return errors::FailedPrecondition(
        "Trying to register filesystem without `cleanup` operation on read "

// Caller must call TF_DeleteTensor() over the returned tensor. If the queue is
// empty, this call is blocked.
//
// Tensors are enqueued via the corresponding TF enqueue op.
// TODO(hongm): Add support for `timeout_ms`.
TF_CAPI_EXPORT extern TF_Tensor* TF_DequeueNamedTensor(TF_Session* session,
                                                       int tensor_id,

      tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h));
  if (handle->Type() != TensorHandle::LOCAL) {
    status->status = tensorflow::errors::InvalidArgument(
        "DLPack doesn't support ", handle->TypeString(), " tensor");
    return nullptr;
  }
  const tensorflow::Tensor* tensor;
  status->status = handle->Tensor(&tensor);
  if (!status->status.ok()) {
    return nullptr;
  }
  return tensor;

  AbstractTensorHandle* GetHandle() const;

 private:
  AbstractTensorHandle* handle_;
};

// A tracing/immediate-execution agnostic tape.
//
// Gradient functions defined for this tape must support handling null incoming
// gradients.
class Tape : protected eager::GradientTape<AbstractTensorHandle,
                                           GradientFunction, TapeTensor> {
 public:

  chmod +x "/usr/local/bin/bazel"
fi

# "TFCI_MACOS_UPGRADE_PYENV_ENABLE" is used to decide if we need to upgrade the
# Pyenv version. We enable this for macOS x86 builds as the default Pyenv on
# those VMs does not support installing Python 3.12 and above which we need
# for running smoke tests in nightly/release wheel builds.
if [[ "${TFCI_MACOS_UPGRADE_PYENV_ENABLE}" == 1 ]]; then