Project: 015_Toward_Guidance-Free_AR_Visual_Generation_via_Cond | Function: train_step (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Core CCA training algorithm implementing Condition Contrastive Alignment for guidance-free autoregressive visual generation with contrastive loss computation

**KEY TERMS:** {
  "img_logps": "log p_θ(x|c) current model positive likelihood",
  "negative_img_logps": "log p_θ(x|c̃) current model negative likelihood",
  "img_ref_logps": "log p_ref(x|c) reference model positive likelihood",
  "negative_img_ref_logps": "log p_ref(x|c̃) reference model negative likelihood",
  "img_logp_gap": "likelihood ratio for positive examples",
  "negative_img_logp_gap": "likelihood ratio for negative examples",
  "F.logsigmoid": "log σ(·) sigmoid activation in loss",
  "self.beta": "β sharpness parameter",
  "self.lambda_": "λ negative example weight"
}

**CONTEXT CODE:** dist = MockDist()

Ten = torch.Tensor
FTen = torch.Tensor
ITen = torch.LongTensor
BTen = torch.BoolTensor


class VAR_CCATrainer(object):
    def __init__(
        self, device, patch_nums: Tuple[int, ...], resos: Tuple[int, ...],
        vae_local: VQVAE, ref_var_wo_ddp: VAR, var_wo_ddp: VAR, var: DDP,
        var_opt: AmpOptimizer, label_smooth: float,
        loss_type: str,
        beta: float, lambda_: float, uncond_ratio: float, # CCA parameters
    ):
        raise NotImplementedError("Function implementation has been masked for testing")
    
    @torch.no_grad()
    def eval_ep(self, ld_val: DataLoader):
        tot = 0
        L_mean, L_tail, acc_mean, acc_tail = 0, 0, 0, 0
        stt = time.time()
        training = self.var_wo_ddp.training
        self.var_wo_ddp.eval()
        for inp_B3HW, label_B in ld_val:
            B, V = label_B.shape[0], self.vae_local.vocab_size
            inp_B3HW = inp_B3HW.to(dist.get_device(), non_blocking=True)
            label_B = label_B.to(dist.get_device(), non_blocking=True)
            
            gt_idx_Bl: List[ITen] = self.vae_local.img_to_idxBl(inp_B3HW)
            gt_BL = torch.cat(gt_idx_Bl, dim=1)
            x_BLCv_wo_first_l: Ten = self.quantize_local.idxBl_to_var_input(gt_idx_Bl)
            
            self.var_wo_ddp.forward
            logits_BLV = self.var_wo_ddp(label_B, x_BLCv_wo_first_l)
            L_mean += self.val_loss(logits_BLV.data.view(-1, V), gt_BL.view(-1)) * B
            L_tail += self.val_loss(logits_BLV.data[:, -self.last_l:].reshape(-1, V), gt_BL[:, -self.last_l:].reshape(-1)) * B
            acc_mean += (logits_BLV.data.argmax(dim=-1) == gt_BL).sum() * (100/gt_BL.shape[1])
            acc_tail += (logits_BLV.data[:, -self.last_l:].argmax(dim=-1) == gt_BL[:, -self.last_l:]).sum() * (100 / self.last_l)
            tot += B
        self.var_wo_ddp.train(training)
        
        stats = L_mean.new_tensor([L_mean.item(), L_tail.item(), acc_mean.item(), acc_tail.item(), tot])
        dist.allreduce(stats)
        tot = round(stats[-1].item())
        stats /= tot
        L_mean, L_tail, acc_mean, acc_tail, _ = stats.tolist()
        return L_mean, L_tail, acc_mean, acc_tail, tot, time.time()-stt
    
    def train_step(
        self, it: int, g_it: int, stepping: bool, metric_lg: MetricLogger, tb_lg: TensorboardLogger,
        inp_B3HW: FTen, label_B: Union[ITen, FTen], prog_si: int, prog_wp_it: float,
    ) -> Tuple[Optional[Union[Ten, float]], Optional[float]]:
        """Core CCA training algorithm implementing Condition Contrastive Alignment for guidance-free autoregressive visual generation with contrastive loss computation"""
        raise NotImplementedError("Function implementation has been masked for testing")
    
    def get_config(self):
        return {
            'patch_nums':   self.patch_nums, 'resos': self.resos,
            'label_smooth': self.label_smooth,
            'prog_it':      self.prog_it, 'last_prog_si': self.last_prog_si, 'first_prog': self.first_prog,
        }
    
    def state_dict(self):
        state = {'config': self.get_config()}
        for k in ('var_wo_ddp',):
            m = getattr(self, k)
            if m is not None:
                if hasattr(m, '_orig_mod'):
                    m = m._orig_mod
                state[k] = m.state_dict()
        return state['var_wo_ddp']
    
    def load_state_dict(self, state, strict=True, skip_vae=False):
        raise NotImplementedError

Complete the function implementation replacing "raise NotImplementedError".