code: def evaluation(
      model, train_config, eval_dataloader, local_rank, tokenizer
  ):
      """
      Evaluates the model on the given dataloader
  
      Args:
          model: The model to evaluate
          eval_dataloader: The dataloader containing the evaluation data
          local_rank: The rank of the current node in a distributed setting
          tokenizer: The tokenizer used to decode predictions
  
      Returns: eval_ppl, eval_epoch_loss
      """
      if train_config.enable_fsdp:
          world_size = int(os.environ["WORLD_SIZE"])
      model.eval()
      eval_preds = []
      val_step_loss = []
      val_step_perplexity = []
      eval_loss = 0.0  # Initialize evaluation loss
      with MemoryTrace() as memtrace:
          for step, batch in enumerate(
              tqdm(
                  eval_dataloader,
                  colour="green",
                  desc="evaluating Epoch",
                  dynamic_ncols=True,
              )
          ):
              for key in batch.keys():
                  if train_config.enable_fsdp:
                      batch[key] = batch[key].to(local_rank)
                  else:
                      if is_xpu_available():
                          batch[key] = batch[key].to("xpu:0")
                      else:
                          batch[key] = batch[key].to("cuda:0")
              # Ensure no gradients are computed for this scope to save memory
              with torch.no_grad():
                  # Forward pass and compute loss
                  outputs = model(**batch)
                  loss = outputs.loss
                  if train_config.save_metrics:
                      val_step_loss.append(loss.detach().float().item())
                      val_step_perplexity.append(
                          float(torch.exp(loss.detach().float()))
                      )
  
                  eval_loss += loss.detach().float()
              # Decode predictions and add to evaluation predictions list
              preds = torch.argmax(outputs.logits, -1)
              eval_preds.extend(
                  tokenizer.batch_decode(
                      preds.detach().cpu().numpy(),
                      skip_special_tokens=True,
                  )
              )
  
      # If there's more than one CUDA device, reduce evaluation loss across all devices
      if is_xpu_available() and (
          torch.xpu.device_count() > 1 and train_config.enable_fsdp
      ):
          dist.all_reduce(eval_loss, op=dist.ReduceOp.SUM)
      if torch.cuda.device_count() > 1 and train_config.enable_fsdp:
          dist.all_reduce(eval_loss, op=dist.ReduceOp.SUM)
  
      # Compute average loss and perplexity
      eval_epoch_loss = eval_loss / len(eval_dataloader)
      if train_config.enable_fsdp:
          eval_epoch_loss = eval_epoch_loss / world_size
      eval_ppl = torch.exp(eval_epoch_loss)
  
      # Print evaluation metrics
      if train_config.enable_fsdp:
          if local_rank == 0:
              print(f" {eval_ppl=} {eval_epoch_loss=}")
      else:
          print(f" {eval_ppl=} {eval_epoch_loss=}")
  
      return (
          eval_ppl,
          eval_epoch_loss,
          val_step_loss,
          val_step_perplexity,
      )
  
imports: import os
  import time
  import yaml
  from contextlib import nullcontext
  from pathlib import Path
  from pkg_resources import packaging
  from datetime import datetime
  import torch
  import torch.cuda.nccl
  import torch.distributed
  from torch.distributed.fsdp import StateDictType
  from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
  from tqdm import tqdm
  from transformers import LlamaTokenizer
  import json
  from llama_recipes.model_checkpointing import save_model_checkpoint, save_model_and_optimizer_sharded, save_optimizer_checkpoint
  from llama_recipes.policies import fpSixteen, bfSixteen, get_llama_wrapper
  from llama_recipes.utils.memory_utils import MemoryTrace
  from accelerate.utils import is_xpu_available, is_ccl_available
label_desc: Use image node_id 4 for calling a global function with example usage: evaluation(model, train_config, eval_dataloader, local_rank, tokenizer) and returns: eval_ppl,  eval_epoch_loss,  val_step_loss,  val_step_perplexity