hunk.code_window: @pytest.mark.timeout(120)
   @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS))
   @pytest.mark.parametrize('batch_size', [1])
   def test_model_forward_torchscript(model_name, batch_size):
       """Run a single forward pass with each model"""
  
hunk.labels: keep keep keep replace keep keep keep
hunk.after_edit: @pytest.mark.parametrize(
       'model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS, name_matches_cfg=True))
  
hunk.file_path: tests/test_models.py
hunk.type: replace
file: import torch
  import math
  import warnings
  
  from torch.nn.init import _calculate_fan_in_and_fan_out
  
  
  def _no_grad_trunc_normal_(tensor, mean, std, a, b):
      # Cut & paste from PyTorch official master until it's in a few official releases - RW
      # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
      def norm_cdf(x):
          # Computes standard normal cumulative distribution function
          return (1. + math.erf(x / math.sqrt(2.))) / 2.
  
      if (mean < a - 2 * std) or (mean > b + 2 * std):
          warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
                        "The distribution of values may be incorrect.",
                        stacklevel=2)
  
      with torch.no_grad():
          # Values are generated by using a truncated uniform distribution and
          # then using the inverse CDF for the normal distribution.
          # Get upper and lower cdf values
          l = norm_cdf((a - mean) / std)
          u = norm_cdf((b - mean) / std)
  
          # Uniformly fill tensor with values from [l, u], then translate to
          # [2l-1, 2u-1].
          tensor.uniform_(2 * l - 1, 2 * u - 1)
  
          # Use inverse cdf transform for normal distribution to get truncated
          # standard normal
          tensor.erfinv_()
  
          # Transform to proper mean, std
          tensor.mul_(std * math.sqrt(2.))
          tensor.add_(mean)
  
          # Clamp to ensure it's in the proper range
          tensor.clamp_(min=a, max=b)
          return tensor
  
  
  def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
      # type: (Tensor, float, float, float, float) -> Tensor
      r"""Fills the input Tensor with values drawn from a truncated
      normal distribution. The values are effectively drawn from the
      normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
      with values outside :math:`[a, b]` redrawn until they are within
      the bounds. The method used for generating the random values works
      best when :math:`a \leq \text{mean} \leq b`.
      Args:
          tensor: an n-dimensional `torch.Tensor`
          mean: the mean of the normal distribution
          std: the standard deviation of the normal distribution
          a: the minimum cutoff value
          b: the maximum cutoff value
      Examples:
          >>> w = torch.empty(3, 5)
          >>> nn.init.trunc_normal_(w)
      """
      return _no_grad_trunc_normal_(tensor, mean, std, a, b)
  
  
  def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
      fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
      if mode == 'fan_in':
          denom = fan_in
      elif mode == 'fan_out':
          denom = fan_out
      elif mode == 'fan_avg':
          denom = (fan_in + fan_out) / 2
  
      variance = scale / denom
  
      if distribution == "truncated_normal":
          # constant is stddev of standard normal truncated to (-2, 2)
          trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
      elif distribution == "normal":
          tensor.normal_(std=math.sqrt(variance))
      elif distribution == "uniform":
          bound = math.sqrt(3 * variance)
          tensor.uniform_(-bound, bound)
      else:
          raise ValueError(f"invalid distribution {distribution}")
  
  
  def lecun_normal_(tensor):
      variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
  