code: def _from_config(cls, config: "PretrainedConfig", **kwargs):
      final_kwargs = {}
      final_kwargs["hidden_size"] = config.hidden_size
      final_kwargs["key_value_proj_dim"] = config.d_kv
  
      final_kwargs["is_decoder"] = getattr(config, "is_decoder", False)
      final_kwargs["has_relative_attention_bias"] = getattr(
          config, "has_relative_attention_bias", True
      )
      final_kwargs["normalize"] = getattr(config, "normalize", True)
      final_kwargs["is_cross_attention"] = getattr(
          config, "is_cross_attention", False
      )
  
      final_kwargs[
          "relative_attention_num_buckets"
      ] = config.relative_attention_num_buckets
      final_kwargs["num_heads"] = config.num_attention_heads
  
      final_kwargs["dropout"] = config.dropout_rate
      final_kwargs.update(**kwargs)
      return cls(**final_kwargs)
  
imports: import math
  from typing import Optional, Tuple, TYPE_CHECKING
  from dataclasses import dataclass
  import torch
  import torch.nn.functional
  from allennlp.common import FromParams
  from allennlp.common.checks import ConfigurationError
  from allennlp.modules.matrix_attention.matrix_attention import MatrixAttention
  from allennlp.modules.transformer.transformer_module import TransformerModule
  from allennlp.modules.transformer.util import apply_mask, FloatT, IntT, BoolT
label_desc: Use image node_id 3 for calling the T5Attention obj's underlying member method code with example usage: obj._from_config(cls, config) and returns: cls