code: def __init__(
      self,
      *,
      batch_size: int,
      input_shape: Tuple[int, ...],
      in_features: int,
      out_features: int,
      bias: bool = True,
      random_seed: Optional[int] = None,
      criterion: Callable = F.cross_entropy,
  ) -> None:
      super().__init__(random_seed=random_seed, criterion=criterion)
      self._input_tensor = torch.randn(
          batch_size, *input_shape, in_features
      )
      self._module = nn.Linear(
          in_features=in_features,
          out_features=out_features,
          bias=bias,
      )
      self._labels = torch.randn(batch_size, *input_shape, out_features)
  
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 LinearBase object from inherited base classes: Layer with example: obj = LinearBase()