code: def filter_parameters(model, filter_string):
      return {
          k: v
          for (k, v) in model.named_parameters()
          if filter_string in k
      }
  
imports: import copy
  import random
  import torch
  from adapters import ADAPTER_MODEL_MAPPING, AutoAdapterModel, PrefixTuningConfig, SeqBnConfig, T5AdapterModel
  from adapters.composition import BatchSplit, Parallel
  from adapters.models.bert_generation.adapter_model import BertGenerationAdapterModel
  from transformers import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, Trainer, TrainingArguments
  from transformers.testing_utils import require_torch, torch_device
label_desc: Use image node_id 1 for calling a global function with example usage: filter_parameters(model, filter_string) and returns: unknown