class_docstr: y_range: Used specifically for regression. = None for classification.
class_docstr_tok: ["y_range",":","Used","specifically","for","regression",".","=","None","for","classification","."]
code: def quantile_loss(self, predict_data, target_data, margin):
      if margin > 0.0:
          m_loss = self.margin_loss(predict_data)
      else:
          m_loss = 0.0
      h_loss = self.huber_pinball_loss(predict_data, target_data).mean()
      return h_loss + margin * m_loss
  
file_path: autogluon/tabular/src/autogluon/tabular/models/tabular_nn/torch/torch_network_modules.py
imports: import logging
  import numpy
  import torch
  import torch.nn
  from autogluon.core.constants import BINARY, MULTICLASS, QUANTILE, REGRESSION, SOFTCLASS
  from ..utils.nn_architecture_utils import get_embed_sizes
label_desc: Use image node_id 7 for calling the EmbedNet obj's underlying member method code with example usage: obj.quantile_loss(predict_data, target_data, margin) and returns: unknown