code: def __init__(
      self, nx, n_ctx, config, scale=False, is_cross_attention=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 implem]
      assert n_state % config.n_head == 0
      self.register_buffer(
          "bias",
          torch.tril(
              torch.ones((n_ctx, n_ctx), dtype=torch.uint8)
          ).view(1, 1, n_ctx, n_ctx),
      )
      self.register_buffer("masked_bias", torch.tensor(-1e4))
      self.n_head = config.n_head
      self.split_size = n_state
      self.scale = scale
      self.is_cross_attention = is_cross_attention
      if self.is_cross_attention:
          self.c_attn = Conv1D(2 * n_state, nx)
          self.q_attn = Conv1D(n_state, nx)
      else:
          self.c_attn = Conv1D(3 * n_state, 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 os
  from dataclasses import dataclass
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from transformers.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from transformers.utils import logging
  from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
label_desc: Use image node_id 1 to create a new Attention object from inherited base classes: nn with example: obj = Attention(nx, n_ctx, config, scale, is_cross_attention)