code: def __init__(
      self,
      is_decoder: bool = False,
      hidden_size: int = 512,
      key_value_proj_dim: int = 64,
      num_heads: int = 8,
      has_relative_attention_bias: bool = False,
      relative_attention_num_buckets: int = 32,
      dropout: float = 0.1,
      normalize: bool = True,
      is_cross_attention: bool = False,
  ):
      if not has_relative_attention_bias:
          relative_attention_num_buckets = None  # type: ignore
  
      super().__init__(
          hidden_size=hidden_size,
          attention_head_size=key_value_proj_dim,
          num_attention_heads=num_heads,
          output_linear=True,
          scoring_func="dot_product",
          dropout=dropout,
          bias=False,
          normalize_weights=normalize,
          is_decoder=is_decoder,
          is_cross_attention=is_cross_attention,
          relative_attention_num_buckets=relative_attention_num_buckets,
      )
  
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 1 to create a new T5Attention object from inherited base classes: AttentionModule with example: obj = T5Attention(is_decoder, hidden_size, key_value_proj_dim, num_heads, has_relative_attention_bias, relative_attention_num_buckets, dropout, normalize, is_cross_attention)