code: def forward(self, batch, training=True):
      if isinstance(batch, dict):
          data = batch["image"].to(self.device)
      else:
          data = batch.to(self.device)
      data = data.float()
      # pred = self.model(data, training=self.training)
      pred = self.model(data, training=training)
  
      # if self.training:
      if training:
          for key, value in batch.items():
              if value is not None:
                  if hasattr(value, "to"):
                      batch[key] = value.to(self.device)
          loss_with_metrics = self.criterion(pred, batch)
          loss, metrics = loss_with_metrics
          return loss, pred, metrics
      return pred
  
file_path: EasyOCR/easyocr/DBNet/model/model.py
imports: import os
  import torch
  import torch.nn
  import torch.nn.functional
  from ..None import backbones
  from ..None import decoders
label_desc: Use image node_id 3 for calling the SegDetectorModel obj's underlying member method code with example usage: obj.forward(batch, training) and returns: pred, loss,  pred,  metrics