tail    []*wantConn
}

// len returns the number of items in the queue.
func (q *wantConnQueue) len() int {
	return len(q.head) - q.headPos + len(q.tail)
}

// pushBack adds w to the back of the queue.
func (q *wantConnQueue) pushBack(w *wantConn) {
	q.tail = append(q.tail, w)
}

// popFront removes and returns the wantConn at the front of the queue.
func (q *wantConnQueue) popFront() *wantConn {

	}
	pInfo := obj.(*framework.QueuedPodInfo)
	pInfo.Attempts++
	p.schedulingCycle++
	// In flight, no concurrent events yet.
	if p.isSchedulingQueueHintEnabled {
		p.inFlightPods[pInfo.Pod.UID] = p.inFlightEvents.PushBack(pInfo.Pod)
	}

	// Update metrics and reset the set of unschedulable plugins for the next attempt.
	for plugin := range pInfo.UnschedulablePlugins.Union(pInfo.PendingPlugins) {

  if (!main_fn) return std::string("");

  flatbuffers::FlatBufferBuilder fb_builder;
  std::vector<mlir::func::FuncOp> funcs;
  funcs.push_back(main_fn);
  module.walk([&](mlir::func::FuncOp fn) {
    if (fn != main_fn) {
      funcs.push_back(fn);
    }
  });

  // Populate the hardware metadata.
  // And collect the hardwares used.
  std::map<std::string, uint8_t> hardware_map;

        int64_t size =
            padding_low[i] + shape[i] + (shape[i] - 1) * padding_interm[i];
        padding_high.push_back(input_dim - size);
      } else {
        dims_to_reverse.push_back(i);
        padding_high.push_back(input_dim - begin_indices[i] - 1);
        padding_interm.push_back(-strides[i] - 1);

        // Pad the lower dimension up to the expected input shape.
        int64_t size =

      std::vector<uint8_t>(expected_value.begin(), expected_value.end());
  input_model.buffers.push_back(std::move(model_metadata_buffer));
  auto metadata_t = std::make_unique<tflite::MetadataT>();
  metadata_t->name = tflite::optimize::kTfLiteReducedPrecisionKey;
  metadata_t->buffer = input_model.buffers.size() - 1;
  input_model.metadata.push_back(std::move(metadata_t));

  // Sparsify and create output model

      if (!buffer_or) return false;
      buffers_.push_back(*buffer_or);
    } else {
      buffers_.push_back(empty_buffer_);
    }

    auto tensor_or =
        BuildTensor(value, tensor_name, buffer_index, quant_parameters);
    if (!tensor_or) return false;
    tensors.push_back(*tensor_or);

    return true;
  };

      // resized.
      if (resized_tensor_list_index.count(i)) {
        updated_types->push_back(
            VariantToUnrankedTensorType(std::get<0>(it), std::get<1>(it)));
      } else {
        updated_types->push_back(std::get<2>(it).getType());
      }
    } else {
      updated_types->push_back(std::get<0>(it));
    }
    ++i;
  }
}

      window_dimensions_vec.push_back(
          window_dimensions.getValues<IntegerAttr>()[i].getInt());
    }

    for (auto i = 0; i < window_strides.size(); ++i) {
      window_strides_vec.push_back(
          window_strides.getValues<IntegerAttr>()[i].getInt());
    }

    for (auto i = 0; i < base_dilations.size(); ++i) {
      base_dilations_vec.push_back(

  llvm::SmallVector<ParsedDevice, 8> matching_devices;
  for (const auto& device : devices)
    if (DeviceNameUtils::IsCompleteSpecification(spec, device))
      matching_devices.push_back(device);
  return matching_devices;
}

// Create error message for a conflicting attribute of a device.
template <typename T>
absl::Status MismatchedTPUSystemAttributeErr(absl::string_view attribute, T a,

  // reduction sent to each replica is over all replicas.
  uint32_t num_replicas = replicate.getN();
  llvm::SmallVector<int32_t, 4> group_assignment_val;
  for (int i = 0; i < num_replicas; ++i) group_assignment_val.push_back(i);
  Value group_assignment = builder.create<ConstOp>(
      block_arg.getLoc(),
      DenseIntElementsAttr::get(
          RankedTensorType::get({1, num_replicas}, builder.getIntegerType(32)),