code: def gen_full_pred_after_one_step(
      model: nn.Module,
      opt: torch.optim.Optimizer,
      input: ModelInput,
  ) -> torch.Tensor:
      # Run a single training step of the global model.
      opt.zero_grad()
      model.train(True)
      loss, _ = model(input)
      loss.backward()
      opt.step()
  
      # Run a forward pass of the global model.
      with torch.no_grad():
          model.train(False)
          full_pred = model(input)
          return full_pred
  
imports: from enum import Enum
  from typing import Any, cast, Dict, List, Optional, Protocol, Tuple, Type, Union
  import torch
  import torch.distributed
  import torch.nn
  from fbgemm_gpu.split_embedding_configs import EmbOptimType
  from torch.distributed.optim import _apply_optimizer_in_backward
  from torchrec.distributed.embedding_types import EmbeddingTableConfig
  from torchrec.distributed.fbgemm_qcomm_codec import CommType, get_qcomm_codecs_registry, QCommsConfig
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.planner import EmbeddingShardingPlanner, ParameterConstraints, Topology
  from torchrec.distributed.test_utils.multi_process import MultiProcessContext
  from torchrec.distributed.test_utils.test_model import ModelInput, TestEBCSharder, TestEBSharder, TestETCSharder, TestETSharder, TestSparseNNBase
  from torchrec.distributed.types import EmbeddingModuleShardingPlan, ModuleSharder, ShardedTensor, ShardingEnv, ShardingPlan, ShardingType
  from torchrec.modules.embedding_configs import BaseEmbeddingConfig, EmbeddingBagConfig
  from torchrec.optim.keyed import CombinedOptimizer, KeyedOptimizerWrapper
  from torchrec.optim.optimizers import in_backward_optimizer_filter
label_desc: Use image node_id 5 for calling a global function with example usage: gen_full_pred_after_one_step(model, opt, input) and returns: full_pred