docstr: labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
      Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
      config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
      `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
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code: def forward(
      self,
      input_ids: torch.LongTensor = None,
      attention_mask: Optional[torch.Tensor] = None,
      position_ids: Optional[torch.LongTensor] = None,
      past_key_values: Optional[List[torch.FloatTensor]] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
      labels: Optional[torch.LongTensor] = None,
      use_cache: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
      r"""
      labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
          Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
          config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
          `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
      """
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      transformer_outputs = self.model(
          input_ids,
          attention_mask=attention_mask,
          position_ids=position_ids,
          past_key_values=past_key_values,
          inputs_embeds=inputs_embeds,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
      hidden_states = transformer_outputs[0]
      logits = self.score(hidden_states)
  
      if input_ids is not None:
          batch_size = input_ids.shape[0]
      else:
          batch_size = inputs_embeds.shape[0]
  
      if self.config.pad_token_id is None and batch_size != 1:
          raise ValueError(
              "Cannot handle batch sizes > 1 if no padding token is defined."
          )
      if self.config.pad_token_id is None:
          sequence_lengths = -1
      else:
          if input_ids is not None:
              sequence_lengths = (
                  torch.ne(input_ids, self.config.pad_token_id).sum(-1)
                  - 1
              ).to(logits.device)
          else:
              sequence_lengths = -1
  
      pooled_logits = logits[
          torch.arange(batch_size, device=logits.device),
          sequence_lengths,
      ]
  
      loss = None
      if labels is not None:
          labels = labels.to(logits.device)
          if self.config.problem_type is None:
              if self.num_labels == 1:
                  self.config.problem_type = "regression"
              elif self.num_labels > 1 and (
                  labels.dtype == torch.long
                  or labels.dtype == torch.int
              ):
                  self.config.problem_type = (
                      "single_label_classification"
                  )
              else:
                  self.config.problem_type = (
                      "multi_label_classification"
                  )
  
          if self.config.problem_type == "regression":
              loss_fct = MSELoss()
              if self.num_labels == 1:
                  loss = loss_fct(
                      pooled_logits.squeeze(), labels.squeeze()
                  )
              else:
                  loss = loss_fct(pooled_logits, labels)
          elif (
              self.config.problem_type == "single_label_classification"
          ):
              loss_fct = CrossEntropyLoss()
              loss = loss_fct(
                  pooled_logits.view(-1, self.num_labels),
                  labels.view(-1),
              )
          elif self.config.problem_type == "multi_label_classification":
              loss_fct = BCEWithLogitsLoss()
              loss = loss_fct(pooled_logits, labels)
      if not return_dict:
          output = (pooled_logits,) + transformer_outputs[1:]
          return ((loss,) + output) if loss is not None else output
  
      return SequenceClassifierOutputWithPast(
          loss=loss,
          logits=pooled_logits,
          past_key_values=transformer_outputs.past_key_values,
          hidden_states=transformer_outputs.hidden_states,
          attentions=transformer_outputs.attentions,
      )
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from transformers.models.llama.configuration_llama import LlamaConfig
label_desc: Use image node_id 4 for calling the LlamaForSequenceClassification obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict) and returns: SequenceClassifierOutputWithPast, unknown