code: def __init__(self, n_models, in_features, out_features, bias):
      super().__init__()
      self.n_models = n_models
      self.out_features = out_features
      self.in_features = in_features
      self.weight = nn.Parameter(
          torch.Tensor(n_models, out_features, in_features)
      )
      if bias:
          self.bias = nn.Parameter(torch.Tensor(n_models, out_features))
      else:
          self.register_parameter("bias", None)
  
imports: import math
  import os
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import numpy
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import functional
  from ....modeling_utils import PreTrainedModel
  from ....utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_trajectory_transformer import TrajectoryTransformerConfig
label_desc: Use image node_id 1 to create a new EinLinear object from inherited base classes: nn with example: obj = EinLinear(n_models, in_features, out_features, bias)