code: def __init__(self, config):
      super().__init__(config)
      num_layers = len(self.model.layers)
      for layer_idx in range(num_layers):
          self.model.layers[
              layer_idx
          ].self_attn = H2OLlamaAttention_streaming(config)
  
imports: import math
  from typing import Optional, Tuple
  import pdb
  import torch
  from torch import nn
  import torch.utils.checkpoint
  import torch.nn.functional
  from transformers.models.llama.modeling_llama import LlamaAttention, rotate_half, apply_rotary_pos_emb, LlamaRotaryEmbedding, apply_rotary_pos_emb, LlamaForCausalLM
  import types
  from transformers.configuration_utils import PretrainedConfig
label_desc: Use image node_id 1 to create a new H2OLlamaForCausalLM_streaming object from inherited base classes: LlamaForCausalLM with example: obj = H2OLlamaForCausalLM_streaming(config)