code: def test_conv1d(
      self,
      N: int,
      C: int,
      W: int,
      out_channels_mapper: Callable[[int], int],
      kernel_size: int,
      stride: int,
      padding: int,
      dilation: int,
      groups: int,
  ):
      if padding == "same" and stride != 1:
          return
      out_channels = out_channels_mapper(C)
      if (
          C % groups != 0 or out_channels % groups != 0
      ):  # since in_channels and out_channels must be divisible by groups
          return
  
      x = torch.randn([N, C, W])
      conv = nn.Conv1d(
          in_channels=C,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=groups,
      )
      self.run_test(
          x,
          conv,
          batch_first=True,
          atol=10e-5,
          rtol=10e-4,
          ew_compatible=N > 0,
      )
  
file_path: opacus/opacus/tests/grad_samples/conv1d_test.py
imports: from typing import Callable
  import hypothesis.strategies
  import torch
  import torch.nn
  from hypothesis import given, settings
  from .common import GradSampleHooks_test, expander, shrinker
label_desc: Use image node_id 1 for calling the Conv1d_test obj's underlying member method code with example usage: obj.test_conv1d(N, C, W, out_channels_mapper, kernel_size, stride, padding, dilation, groups) without return types