code: def __init__(
      self,
      *,
      batch_size: int,
      num_features: int,
      input_shape: Tuple[int, ...],
      eps: float = 1e-05,
      affine: bool = False,
      track_running_stats: bool = False,
      random_seed: Optional[int] = None,
      criterion: Callable = F.cross_entropy,
  ) -> None:
      super().__init__(random_seed=random_seed, criterion=criterion)
  
      D = len(input_shape)
      if D == 1:
          self._module_name = nn.InstanceNorm1d
      elif D == 2:
          self._module_name = nn.InstanceNorm2d
      elif D == 3:
          self._module_name = nn.InstanceNorm3d
      else:
          raise Exception("Input shape must be between 1 and 3 long")
  
      self._input_tensor = torch.randn(
          batch_size, num_features, *input_shape
      )
      self._module = self._module_name(
          num_features=num_features,
          eps=eps,
          affine=affine,
          track_running_stats=track_running_stats,
      )
      self._labels = torch.randn(self._input_tensor.shape)
  
imports: from enum import Enum
  from typing import Callable, Dict, Optional, Tuple, Type, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from opacus.grad_sample import GradSampleModule
  from opacus.grad_sample.utils import wrap_model
  from opacus.layers import DPGRU, DPLSTM, DPRNN, DPMultiheadAttention
  from opacus.layers.dp_rnn import DPRNNBase
label_desc: Use image node_id 1 to create a new InstanceNormBase object from inherited base classes: Layer with example: obj = InstanceNormBase()