code: def _attn(
      self, query, key, value, attention_mask=None, head_mask=None
  ):
      if head_mask is not None:
          # The super dispatch is done in the forward.
          raise ValueError(
              "PyTorch SDPA does not support head_mask. Please open an issue in Transformers repository."
          )
  
      scale = None
      if not self.scale_attn_weights:
          scale = 1
  
      # MQA models: (batch_size, query_length, num_heads * head_dim)
      # MHA models: (batch_size, num_heads, query_length, head_dim)
      query_shape = query.shape
      batch_size = query_shape[0]
      key.shape[-2]
  
      if self.multi_query:
          query_length = query_shape[1]
  
          # SDPA requires the dimension [..., sequence_length, head_dim].
          query = query.view(
              batch_size, query_length, self.num_heads, self.head_dim
          ).transpose(1, 2)
  
          # Without these unsqueeze, SDPA complains as the query and key/value have a different number of dimensions.
          key = key.unsqueeze(1)
          value = value.unsqueeze(1)
  
          # Although these expand are not numerically useful, PyTorch 2.1 can not dispatch to memory-efficient backend
          # and flash attention backend (No available kernel.  Aborting execution.) from the shapes
          # query = [batch_size, num_heads, query_length, head_dim]
          # key = [batch_size, 1, past_length, head_dim]
          # value = [batch_size, 1, past_length, head_dim]
          #
          # so we could do:
          #
          # key = key.expand(-1, self.num_heads, -1, -1)
          # value = value.expand(-1, self.num_heads, -1, -1)
          #
          # However SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
          # so we always dispatch to the math path: https://github.com/pytorch/pytorch/issues/112577.
          # Arguably we could still do expand + contiguous when `query.device.type == "cuda"` in order to dispatch on memory-efficient
          # backend, but it feels very hacky.
      else:
          query_length = query_shape[-1]
  
          # See the comment above.
          if query.device.type == "cuda" and attention_mask is not None:
              query = query.contiguous()
              key = key.contiguous()
              value = value.contiguous()
  
      sdpa_result = torch.nn.functional.scaled_dot_product_attention(
          query,
          key,
          value,
          attn_mask=attention_mask,
          dropout_p=self.attn_pdrop if self.training else 0.0,
          # The query_length > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case query_length == 1.
          is_causal=self.is_causal
          and attention_mask is None
          and query_length > 1,
          scale=scale,
      )
  
      if self.multi_query:
          # (batch_size, num_heads, seq_len, head_dim) --> (batch_size, seq_len, num_heads, head_dim)
          sdpa_result = sdpa_result.transpose(1, 2)
  
          # Reshape is kind of expensive here, as it does a memory copy,
          # but I did not manage to make away without it (logits do not match when using view)
          # (batch_size, seq_len, num_heads, head_dim) --> (batch_size, seq_len, num_heads * head_dim)
          sdpa_result = sdpa_result.reshape(query_shape)
  
      return sdpa_result, None
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_attn_mask_utils import AttentionMaskConverter
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging
  from .configuration_gpt_bigcode import GPTBigCodeConfig
label_desc: Use image node_id 1 for calling the GPTBigCodeSdpaAttention obj's underlying member method code with example usage: obj._attn(query, key, value, attention_mask, head_mask) and returns: sdpa_result,  None