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 init_params(self):
      for layer in self.children():
          if hasattr(layer, "reset_parameters"):
              layer.reset_parameters()
  
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 3 for calling the EmbedNet obj's underlying member method code with example usage: obj.init_params() without return types