code: def test_linear_1d_input(
      self, input_size, weight_output_dim, use_bias
  ):
      input_data = torch.rand(input_size)
      weight = torch.rand((weight_output_dim, input_data.shape[-1]))
      if use_bias:
          bias = torch.rand(weight_output_dim)
      else:
          bias = None
      ref_result = F.linear(input_data, weight, bias)
      packed_weight_bias = torch.ops.prepacked.linear_clamp_prepack(
          weight, bias
      )
      output_linearprepacked = torch.ops.prepacked.linear_clamp_run(
          input_data, packed_weight_bias
      )
      torch.testing.assert_close(
          ref_result, output_linearprepacked, rtol=1e-2, atol=1e-3
      )
  
imports: import unittest
  import torch
  import torch.backends.xnnpack
  from torch.nn import functional
  from torch.utils.mobile_optimizer import optimize_for_mobile
  from torch.testing import FileCheck
  import torch.testing._internal.hypothesis_utils
  from torch.testing._internal.common_utils import TestCase, run_tests, slowTest
  from hypothesis import given, assume
  from hypothesis import strategies
  import io
  import itertools
  from torch.testing._internal.common_utils import IS_FBCODE, TEST_WITH_TSAN
label_desc: Use image node_id 2 for calling the TestXNNPACKOps obj's underlying member method code with example usage: obj.test_linear_1d_input(input_size, weight_output_dim, use_bias) without return types