code: def flatten_output(self, output):
      flatten = []
      for x in output:
          if isinstance(x, (tuple, list)):
              flatten += self.flatten_output(x)
          elif not isinstance(x, torch.Tensor):
              continue
          else:
              flatten.append(x)
      return flatten
  
imports: import operator
  import unittest
  import torch
  from parameterized import parameterized
  from transformers import AutoModel, AutoModelForImageClassification, AutoTokenizer, BertModel
  from transformers.models.bert.modeling_bert import BertSelfAttention
  from transformers.utils.fx import symbolic_trace
  from optimum.fx.optimization import ChangeTrueDivToMulByInverse, FuseBatchNorm1dInLinear, FuseBatchNorm2dInConv2d, FuseBiasInLinear, MergeLinears, ReversibleTransformation, Transformation, compose
  from optimum.fx.utils import are_fx_features_available
label_desc: Use image node_id 1 for calling the TransformationTester obj's underlying member method code with example usage: obj.flatten_output(output) and returns: flatten