code: def __init__(self, nx, n_positions, config, scale=False):
      super().__init__()
      n_state = nx  # in Attention: n_state=768 (nx=n_embd)
      # [switch nx => n_state from Block to Attention to keep identical to TF implementation]
      if n_state % config.n_head != 0:
          raise ValueError(
              f"Attention n_state shape: {n_state} must be divisible by config.n_head {config.n_head}"
          )
      self.register_buffer(
          "bias",
          torch.tril(torch.ones(n_positions, n_positions)).view(
              1, 1, n_positions, n_positions
          ),
      )
      self.n_head = config.n_head
      self.split_size = n_state
      self.scale = scale
  
      self.c_attn = Conv1D(n_state * 3, nx)
      self.c_proj = Conv1D(n_state, nx)
      self.attn_dropout = nn.Dropout(config.attn_pdrop)
      self.resid_dropout = nn.Dropout(config.resid_pdrop)
      self.pruned_heads = set()
  
imports: import json
  import math
  import os
  from dataclasses import dataclass
  from typing import Any, Dict, Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import gelu_new, silu
  from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
  from ...modeling_utils import PreTrainedModel, SequenceSummary
  from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
  from ...utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_openai import OpenAIGPTConfig
label_desc: Use image node_id 1 to create a new Attention object from inherited base classes: nn with example: obj = Attention(nx, n_positions, config, scale)