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 _set_params(
      self,
      num_layers=4,
      hidden_size=128,
      activation="relu",
      use_batchnorm=False,
      dropout_prob=0.1,
      y_range=None,
      alpha=0.01,
      max_embedding_dim=100,
      embed_exponent=0.56,
      embedding_size_factor=1.0,
  ):
      return dict(
          num_layers=num_layers,
          hidden_size=hidden_size,
          activation=activation,
          use_batchnorm=use_batchnorm,
          dropout_prob=dropout_prob,
          y_range=y_range,
          alpha=alpha,
          max_embedding_dim=max_embedding_dim,
          embed_exponent=embed_exponent,
          embedding_size_factor=embedding_size_factor,
      )
  
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 2 for calling the EmbedNet obj's underlying member method code with example usage: obj._set_params(num_layers, hidden_size, activation, use_batchnorm, dropout_prob, y_range, alpha, max_embedding_dim, embed_exponent, embedding_size_factor) and returns: dict