code: def __init__(self, config):
      super().__init__()
      self.ln1 = nn.LayerNorm(config.n_embd)
      self.ln2 = nn.LayerNorm(config.n_embd)
      self.attn = CausalSelfAttention(config)
  
      # MLP
      self.l1 = nn.Linear(config.n_embd, 4 * config.n_embd)
      self.act = nn.GELU()
      self.l2 = nn.Linear(4 * config.n_embd, config.n_embd)
      self.drop = nn.Dropout(config.resid_pdrop)
  
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 Block object from inherited base classes: nn with example: obj = Block(config)