class_docstr: Wrap convolution layers bigger than the provided number of parameters
class_docstr_tok: ["Wrap","convolution","layers","bigger","than","the","provided","number","of","parameters"]
code: def __call__(
      self,
      module: nn.Module,
      recurse: bool,
      unwrapped_params: int,
      **kwargs,
  ):
      is_large = unwrapped_params >= self.threshold
      force_leaf_modules = default_auto_wrap_policy.FORCE_LEAF_MODULES
      if recurse:
          # We should recurse if the module is big enough but not in force_leaf_modules.
          return is_large and not isinstance(
              module, tuple(force_leaf_modules)
          )
      else:
          # If we are not recursing, we should wrap but not the exclude list.
          is_conv = isinstance(module, nn.Conv2d)
          return is_conv and is_large
  
file_path: vissl/vissl/utils/fsdp_utils.py
imports: import torch.distributed
  import torch.nn
  from fairscale.nn import auto_wrap, default_auto_wrap_policy, enable_wrap
  from fairscale.nn.data_parallel import auto_wrap_bn, FullyShardedDataParallel
  from vissl.config.attr_dict import AttrDict
label_desc: Use image node_id 2 for calling the _BigConvAutoWrapPolicy obj's underlying member method code with example usage: obj.__call__(module, recurse, unwrapped_params) and returns: unknown, unknown