code: def _get_mask_value(self, device, dtype):
      # torch.where expects a tensor. We use a cache to avoid recreating it every time.
      if (
          self.mask_value is None
          or self.mask_value.dtype != dtype
          or self.mask_value.device != device
      ):
          self.mask_value = torch.full(
              [], torch.finfo(dtype).min, dtype=dtype, device=device
          )
      return self.mask_value
  
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 2 for calling the GPTBigCodeAttention obj's underlying member method code with example usage: obj._get_mask_value(device, dtype) and returns: self