docstr: Perform inference for a batch.
  
  Parameters
  ----------
  batch
      The batch data.
  model
      A Pytorch model. This is to align with matcher which passes either query or response model.
  precision
      The desired precision used in inference.
  num_gpus
      Number of GPUs.
  
  Returns
  -------
  Model output.
docstr_tok: ["Perform","inference","for","a","batch",".","Parameters","--","--","--","--","--","batch","The","batch","data",".","model","A","Pytorch","model",".","This","is","to","align","with","matcher","which","passes","either","query","or","response","model",".","precision","The","desired","precision","used","in","inference",".","num_gpus","Number","of","GPUs",".","Returns","--","--","--","-","Model","output","."]
code: def predict_batch(
      self,
      batch: Dict,
      model: nn.Module,
      precision: Union[str, int],
      num_gpus: int,
  ):
      """
      Perform inference for a batch.
  
      Parameters
      ----------
      batch
          The batch data.
      model
          A Pytorch model. This is to align with matcher which passes either query or response model.
      precision
          The desired precision used in inference.
      num_gpus
          Number of GPUs.
  
      Returns
      -------
      Model output.
      """
      device_type = "cuda" if torch.cuda.is_available() else "cpu"
      device = torch.device(device_type)
      batch_size = len(batch[next(iter(batch))])
      if 1 < num_gpus <= batch_size:
          model = nn.DataParallel(model)
      model.to(device).eval()
      batch = move_to_device(batch, device=device)
      precision_context = get_precision_context(
          precision=precision, device_type=device_type
      )
      with precision_context, torch.no_grad():
          output = run_model(model, batch)
          if (
              hasattr(self, "_model_postprocess_fn")
              and self._model_postprocess_fn
          ):
              output = self._model_postprocess_fn(output)
  
      if isinstance(model, nn.DataParallel):
          model = model.module
      else:
          model = model
      output = move_to_device(output, device=torch.device("cpu"))
      return output[model.prefix]
  
file_path: autogluon/multimodal/src/autogluon/multimodal/utils/inference.py
imports: import logging
  from typing import Callable, Dict, List, Optional, Tuple, Union
  import pandas
  import torch
  from omegaconf import OmegaConf
  from scipy.special import softmax
  from torch import nn
  from ..constants import BBOX, COLUMN_FEATURES, FEATURES, IMAGE, IMAGE_META, LOGITS, MASKS, NER_ANNOTATION, NER_RET, OVD_RET, PROBABILITY, PROMPT, QUERY, RESPONSE, SCORE, SEMANTIC_MASK, TEXT, TOKEN_WORD_MAPPING, WORD_OFFSETS
  from ..data.preprocess_dataframe import MultiModalFeaturePreprocessor
  from ..data.utils import apply_data_processor, apply_df_preprocessor, get_collate_fn, get_per_sample_features
  from ..models.utils import run_model
  from .environment import get_precision_context, move_to_device
  from .matcher import compute_matching_probability
  from .misc import tensor_to_ndarray
label_desc: Use image node_id 1 for calling the RealtimeMixin obj's underlying member method code with example usage: obj.predict_batch(batch, model, precision, num_gpus) and returns: output