code: def test_fuse_bias_in_linear():
      _, traced = get_bert_model()
      num_bias_in_linears = sum(
          int(mod.bias is not None)
          for mod in traced.modules()
          if isinstance(mod, torch.nn.Linear)
      )
      assert (
          num_bias_in_linears != 0
      ), "there should be biases in at least one linear module in the model to actually perform the test"
      transformation = FuseBiasInLinear()
      transformed = transformation(traced)
      num_bias_in_linears = sum(
          int(mod.bias is not None)
          for mod in transformed.modules()
          if isinstance(mod, torch.nn.Linear)
      )
      assert (
          num_bias_in_linears == 0
      ), "there should not be any bias left in any linear module now"
  
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 3 for calling a global function with example usage: test_fuse_bias_in_linear() without return types