code: def forward(self, input: Tensor) -> Tensor:
      self._check_input_dim(input)
  
      feature_dim = input.dim() - self._get_no_batch_dim()
      if input.size(feature_dim) != self.num_features:
          if self.affine:
              raise ValueError(
                  f"expected input's size at dim={feature_dim} to match num_features"
                  f" ({self.num_features}), but got: {input.size(feature_dim)}."
              )
          else:
              warnings.warn(
                  f"input's size at dim={feature_dim} does not match num_features. "
                  "You can silence this warning by not passing in num_features, "
                  "which is not used because affine=False"
              )
  
      if input.dim() == self._get_no_batch_dim():
          return self._handle_no_batch_input(input)
  
      return self._apply_instance_norm(input)
  
imports: import warnings
  from torch import Tensor
  from .batchnorm import _LazyNormBase, _NormBase
  from ..None import functional
label_desc: Use image node_id 7 for calling the _InstanceNorm obj's underlying member method code with example usage: obj.forward(input) and returns: self, self