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 {

	// lock.
	if atomic.Load(&gcBlackenEnabled) == 0 {
		unlock(&work.assistQueue.lock)
		return true
	}

	gp := getg()
	oldList := work.assistQueue.q
	work.assistQueue.q.pushBack(gp)

	// Recheck for background credit now that this G is in
	// the queue, but can still back out. This avoids a
	// race in case background marking has flushed more
	// credit since we checked above.

	}
	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) {

    grad_outputs->push_back(dx);
    auto dy =
        MatMul(scope, y0, y1, MatMul::TransposeA(adj_y0).TransposeB(adj_y1));
    grad_outputs->push_back(dy);
  } else {
    auto dx = BatchMatMulV3(scope, x0, x1, x_data_type,
                            BatchMatMulV3::AdjX(adj_x0).AdjY(adj_x1));
    grad_outputs->push_back(dx);
    auto dy = BatchMatMulV3(scope, y0, y1, y_data_type,

              static_cast<int64_t>(0)));
    }
    compilation_key_ops.push_back(compilation_key_op);
    core_to_compilation_key.push_back(compilation_key);
    core_to_device_ordinal_op.push_back(device_ordinal_op);
    if (device_cluster->getParentOfType<mlir::tf_device::ReplicateOp>())
      core_to_device_ordinal.push_back(
          core_to_device_ordinal_op[core]->getResults()[0]);
  }

      padded_begin.push_back(0);
      padded_end.push_back(0);
      padded_stride.push_back(1);
    }

    // Account for ellipsis mask.
    ++index;

    // After the ellipsis.
    for (; index < begin_shape[0];) {
      padded_begin.push_back(begin_dense_elem_attr.getValues<int32_t>()[index]);
      padded_end.push_back(end_dense_elem_attr.getValues<int32_t>()[index]);

    });

    std::vector<std::string> lines;
    lines.push_back("-------- Quantization Summary --------");
    lines.push_back("Number of quantized layers in the model");
    lines.push_back("--------------------------------");
    lines.push_back(
        absl::StrFormat("%s Count/Total", pad_string("Name", name_col_width)));
    lines.push_back("================================");

  min_maxs.reserve(mins.size() * 2);
  for (int i = 0, end = mins.size(); i < end; ++i) {
    llvm::APFloat min(mins[i]);
    llvm::APFloat max(maxs[i]);
    min_maxs.push_back(min);
    min_maxs.push_back(max);
  }
  // The layer stats contain only the first min/max pairs.
  mlir::ElementsAttr layer_stats = mlir::DenseFPElementsAttr::get(
      tensorflow::GetTypeFromTFTensorShape({2}, b.getF32Type()),

      absl::string_view frame_name = control_flow_info_[out_id].frame_name;
      if (IsRootEnter(out)) {
        ready_enters_per_frame[frame_name].push_back(out);
      } else if (IsRootExit(out)) {
        ready_exits.push_back(out);
      } else {
        ready.push_back(out);
      }
    }

    if (ready.empty()) {
      // Try moving nodes from ready_enters_per_frame and ready_exits to
      // `ready`.

      reshaped_shape_ints.push_back(outer_shape_ints[i]);
      reshaped_shape_ints.push_back(block_shape_ints[i]);
    }
    for (int64_t i = 1 + block_rank; i < input_rank; ++i) {
      reshaped_shape_vals.push_back(padded_shape_splits[i]);
      reshaped_shape_ints.push_back(padded_shape[i]);
    }
    auto reshaped_shape = ValuesToRank1(