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 predict(self, input_data):
      self.eval()
      with torch.no_grad():
          predict_data = self(input_data)
          if self.problem_type == QUANTILE:
              predict_data = torch.sort(predict_data, -1)[
                  0
              ]  # sorting ensures monotonicity of quantile estimates
          elif self.problem_type in [BINARY, MULTICLASS, SOFTCLASS]:
              predict_data = self.softmax(
                  predict_data
              )  # convert NN output to probability
          elif self.problem_type == REGRESSION:
              predict_data = predict_data.flatten()
          if self.problem_type == BINARY:
              predict_data = predict_data[:, 1]
          return predict_data.data.cpu().numpy()
  
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 9 for calling the EmbedNet obj's underlying member method code with example usage: obj.predict(input_data) and returns: predict_data