code: def forward(self, preds: Tensor, target: Tensor) -> Tensor:
      return torch.mean(torch.square(preds - target))
  
imports: import os
  import tempfile
  from typing import Dict
  import torch
  from marshmallow_dataclass import dataclass
  from torch import nn, Tensor
  from ludwig.api import LudwigModel
  from ludwig.combiners.combiners import Combiner, register_combiner
  from ludwig.constants import BATCH_SIZE, ENCODER_OUTPUT, LOGITS, MINIMIZE, NUMBER, TRAINER
  from ludwig.decoders.base import Decoder
  from ludwig.decoders.registry import register_decoder
  from ludwig.encoders.base import Encoder
  from ludwig.encoders.registry import register_encoder
  from ludwig.modules.loss_modules import LogitsInputsMixin, register_loss
  from ludwig.modules.metric_modules import LossMetric, register_metric
  from ludwig.schema import utils
  from ludwig.schema.combiners.base import BaseCombinerConfig
  from ludwig.schema.combiners.utils import register_combiner_config
  from ludwig.schema.decoders.base import BaseDecoderConfig
  from ludwig.schema.decoders.utils import register_decoder_config
  from ludwig.schema.encoders.base import BaseEncoderConfig
  from ludwig.schema.encoders.utils import register_encoder_config
  from ludwig.schema.features.loss.loss import BaseLossConfig
  from ludwig.schema.features.loss.loss import register_loss
  from tests.integration_tests.utils import category_feature, generate_data, LocalTestBackend, number_feature, sequence_feature
label_desc: Use image node_id 2 for calling the CustomLoss obj's underlying member method code with example usage: obj.forward(preds, target) and returns: torch