code: def compute_preds(self, x, y, negatives):
      neg_is_pos = (y == negatives).all(-1)
      y = y.unsqueeze(0)
      targets = torch.cat([y, negatives], dim=0)
  
      logits = torch.cosine_similarity(
          x.float(), targets.float(), dim=-1
      )
      logits = logits / self.logit_temp
      logits = logits.type_as(x)
  
      if is_xla_tensor(logits) or neg_is_pos.any():
          if not hasattr(self, "_inftensor"):
              fillval = -float(2**30)
              self._inftensor = (
                  torch.tensor(fillval).to(x.device)
                  if is_xla_tensor(logits)
                  else float("-inf")
              )
          logits[1:] = index_put(
              logits[1:], neg_is_pos, self._inftensor
          )
  
      return logits
  
imports: import math
  from dataclasses import dataclass, field
  from typing import List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.data.data_utils import compute_mask_indices
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.distributed import fsdp_wrap
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.distributed.fully_sharded_data_parallel import FullyShardedDataParallel
  from fairseq.modules import Fp32GroupNorm, Fp32LayerNorm, GradMultiply, GumbelVectorQuantizer, LayerNorm, MultiheadAttention, RelPositionalEncoding, SamePad, TransposeLast
  from fairseq.modules.checkpoint_activations import checkpoint_wrapper
  from fairseq.modules.conformer_layer import ConformerWav2Vec2EncoderLayer
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import buffered_arange, index_put, is_xla_tensor
  from .utils import pad_to_multiple
label_desc: Use image node_id 6 for calling the Wav2Vec2Model obj's underlying member method code with example usage: obj.compute_preds(x, y, negatives) and returns: logits