code: def test_metaconv1d_params(bias, padding_mode):
      padding = 0 if padding_mode is None else 2
      padding_mode = padding_mode or "zeros"
      meta_model = MetaConv1d(
          2,
          3,
          kernel_size=5,
          padding=padding,
          padding_mode=padding_mode,
          bias=bias,
      )
      model = nn.Conv1d(
          2,
          3,
          kernel_size=5,
          padding=padding,
          padding_mode=padding_mode,
          bias=bias,
      )
  
      params = OrderedDict()
      params["weight"] = torch.randn(3, 2, 5)
      model.weight.data.copy_(params["weight"])
  
      if bias:
          params["bias"] = torch.randn(3)
          model.bias.data.copy_(params["bias"])
  
      inputs = torch.randn(7, 2, 11)
  
      outputs_torchmeta = meta_model(inputs, params=params)
      outputs_nn = model(inputs)
  
      np.testing.assert_equal(
          outputs_torchmeta.detach().numpy(),
          outputs_nn.detach().numpy(),
      )
  
file_path: pytorch-meta/torchmeta/tests/modules/test_conv.py
imports: import pytest
  import numpy
  import torch
  import torch.nn
  from collections import OrderedDict
  from torchmeta.modules import MetaModule
  from torchmeta.modules.conv import MetaConv1d, MetaConv2d, MetaConv3d
label_desc: Use image node_id 2 for calling a global function with example usage: test_metaconv1d_params(bias, padding_mode) without return types