code: def perform_optimizer_step(
      self, optimizer: Any, num_gradients: Any
  ) -> Any:
      return (
          (optimizer is not None)
          and (
              not self.weight_prediction
              and num_gradients % self.min_update_interval == 0
          )
          or (
              self.weight_prediction
              and num_gradients % self.chunks == 0
          )
      )
  
imports: import time
  from typing import Any, Dict, List, Tuple, Union
  import torch
  from torch import nn
  from torch.autograd.profiler import record_function
  from torch.distributed import ProcessGroup
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from fairscale.nn.model_parallel import get_pipeline_parallel_ranks
  from fairscale.nn.pipe.async_schedule import AsyncMessageBody, AsyncMessageType, AsyncRecvOperator, Location, ModuleWrapper
  from fairscale.nn.pipe.checkpoint import Checkpointing
  from fairscale.nn.pipe.messages import Transport
  from fairscale.nn.pipe.microbatch import Batch
  from fairscale.nn.pipe.types import EVENT_LOOP_ACTIVATIONS_QUEUE, EVENT_LOOP_GRADIENTS_QUEUE, PipeMessage, TensorOrTensors
  from fairscale.nn.pipe.worker import Task
label_desc: Use image node_id 2 for calling the AsyncAMPnetEventLoop obj's underlying member method code with example usage: obj.perform_optimizer_step(optimizer, num_gradients) and returns: unknown