code: def test_same_as_pytorch(self):
      for _ in range(5):
          kernel_size = random.randrange(4, 128)
          batch_size = random.randrange(1, 6)
          length = random.randrange(kernel_size, 1024)
          chin = random.randrange(1, 12)
          chout = random.randrange(1, 12)
          block_ratio = random.choice([5, 10, 20])
          bias = random.random() < 0.5
          if random.random() < 0.5:
              padding = 0
          else:
              padding = random.randrange(
                  kernel_size // 2, 2 * kernel_size
              )
          x = th.randn(batch_size, chin, length)
          w = th.randn(chout, chin, kernel_size)
          keys = [
              "length",
              "kernel_size",
              "chin",
              "chout",
              "block_ratio",
              "bias",
          ]
          loc = dict(locals())
          state = {key: loc[key] for key in keys}
          if bias:
              bias = th.randn(chout)
          else:
              bias = None
          for stride in [1, 2, 5]:
              state["stride"] = stride
              self.compare_pytorch(
                  x,
                  w,
                  bias,
                  stride,
                  padding,
                  block_ratio=block_ratio,
                  msg=repr(state),
              )
  
file_path: julius/tests/test_fftconv.py
imports: import random
  import unittest
  import torch
  from torch.nn import functional
  import julius
label_desc: Use image node_id 1 for calling the TestFFTConv1d obj's underlying member method code with example usage: obj.test_same_as_pytorch() without return types