class_docstr: AsymWeightLoss is an asymmetric loss.
code: def forward(self, y_hat, y):
      if self.L1:
          loss = (
              F.relu(y_hat - y)
              + F.relu(y - y_hat) * self.underestimation_penalty
          )
      else:
          loss = (
              torch.pow(F.relu(y_hat - y), 2)
              + torch.pow(F.relu(y - y_hat), 2)
              * self.underestimation_penalty
          )
      return torch.mean(loss)
  
file_path: BigDL/python/chronos/src/bigdl/chronos/pytorch/loss/asym_weight.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from bigdl.nano.utils.common import invalidInputError
label_desc: Use image node_id 2 for calling the AsymWeightLoss obj's underlying member method code with example usage: obj.forward(y_hat, y) and returns: torch