"description": "Quobyte represents a Quobyte mount on the host that shares a pod's lifetime"
        },
        "rbd": {
          "$ref": "#/definitions/io.k8s.api.core.v1.RBDVolumeSource",
          "description": "RBD represents a Rados Block Device mount on the host that shares a pod's lifetime. More info: https://examples.k8s.io/volumes/rbd/README.md"
        },
        "scaleIO": {

	}
	return buffer.Bytes()
}

// truncatePodHostnameIfNeeded truncates the pod hostname if it's longer than 63 chars.
func truncatePodHostnameIfNeeded(podName, hostname string) (string, error) {
	// Cap hostname at 63 chars (specification is 64bytes which is 63 chars and the null terminating char).
	const hostnameMaxLen = 63
	if len(hostname) <= hostnameMaxLen {
		return hostname, nil
	}

that can be converted to type <code>T</code>.
</p>

<pre class="ebnf">
Conversion = Type "(" Expression [ "," ] ")" .
</pre>

<p>
If the type starts with the operator <code>*</code> or <code>&lt;-</code>,
or if the type starts with the keyword <code>func</code>
and has no result list, it must be parenthesized when
necessary to avoid ambiguity:
</p>

<pre>

  }

  std::vector<std::optional<std::vector<int>>> shapes;
  TF_RETURN_IF_ERROR(::tensorflow::ParseNodeShapes(input_shapes, shapes));

  for (const auto& shape : shapes) {
    if (!shape) {
      return absl::AbortedError("Missing input argument shapes");
    }
    parsed_shapes.push_back(SmallVector<int64_t>(shape->begin(), shape->end()));
  }
  return parsed_shapes;
}

  {
    GraphDefBuilder shape2(GraphDefBuilder::kFailImmediately);
    Node* key_constant = KeyPlaceholder("F1", shape2.opts());
    Node* recv1 = RecvAtHost(
        ops::NodeOut(key_constant, 0), "F1", "F1", "O1", {DT_FLOAT, DT_FLOAT},
        shape2.opts().WithAttr(kXlaHasHostTransferAttrName, true));
    Node* e = Binary(ops::NodeOut(recv1, 0), ops::NodeOut(recv1, 1),
                     shape2.opts()

  std::vector<PartialTensorShape> shapes;
  shapes.reserve(num_shapes);
  for (int i = 0; i < num_shapes; ++i) {
    if (num_dims[i] < 0) {
      shapes.emplace_back();
    } else {
      shapes.emplace_back(ArraySlice<int64_t>(
          reinterpret_cast<const int64_t*>(dims[i]), num_dims[i]));
    }
  }
  desc->node_builder.Attr(attr_name, shapes);
}

  // Check all elements besides at concat_dim match across all shape tensors.
  SmallVector<int32_t, 4> shape0;
  shape0.reserve(num_dims);
  for (int32_t dim : shapes.front().getValues<int32_t>()) shape0.push_back(dim);

  for (DenseIntElementsAttr shape : llvm::drop_begin(shapes, 1)) {
    for (const auto& dims_and_idx : llvm::enumerate(llvm::zip(shape0, shape))) {
      if (dims_and_idx.index() == concat_dim) continue;

    }

    std::vector<PartialTensorShape> shapes;
    if (!GetNodeAttr(e->src()->attrs(), kXlaInferredShapesAttrName, &shapes)
             .ok()) {
      return std::nullopt;
    }

    const PartialTensorShape shape = shapes[e->src_output()];
    if (!shape.IsFullyDefined()) {
      return std::nullopt;
    }

    results[e->dst_input()] = shape;
  }
  return results;
}

// 'shape' is the original shape with padding to match result shape.
int64_t GetElementIndex(const std::vector<int64_t>& shape,
                        const std::vector<int64_t>& current_index) {
  int64_t ind = 0;
  int64_t mul = 1;
  for (int i = shape.size() - 1; i >= 0; --i) {
    ind += (current_index[i] % shape[i]) * mul;
    mul *= shape[i];
  }
  return ind;
}

		}
	}
	lockInit(&pp.timers.mu, lockRankTimers)

	// This P may get timers when it starts running. Set the mask here
	// since the P may not go through pidleget (notably P 0 on startup).
	timerpMask.set(id)
	// Similarly, we may not go through pidleget before this P starts
	// running if it is P 0 on startup.
	idlepMask.clear(id)
}