class_docstr: Conditional Ordinal Regression for Neural networks, used for ordered cateogry values.
  
  Source:
  Xintong Shi, Wenzhi Cao, and Sebastian Raschka (2021).
  Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities.
  Arxiv preprint; https://arxiv.org/abs/2111.08851
class_docstr_tok: ["Conditional","Ordinal","Regression","for","Neural","networks",",","used","for","ordered","cateogry","values",".","Source",":","Xintong","Shi",",","Wenzhi","Cao",",","and","Sebastian","Raschka","(","2021",")",".","Deep","Neural","Networks","for","Rank-Consistent","Ordinal","Regression","Based","On","Conditional","Probabilities",".","Arxiv","preprint",";","https",":","\/\/arxiv.org\/abs\/2111.08851"]
code: def class_similarities_temperature(self) -> int:
      return 0
  
imports: from typing import Dict, List, Type, Union
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import BINARY, BINARY_WEIGHTED_CROSS_ENTROPY, CATEGORY, CORN, HUBER, MEAN_ABSOLUTE_ERROR, MEAN_ABSOLUTE_PERCENTAGE_ERROR, MEAN_SQUARED_ERROR, NEXT_TOKEN_SOFTMAX_CROSS_ENTROPY, NUMBER, ROOT_MEAN_SQUARED_ERROR, ROOT_MEAN_SQUARED_PERCENTAGE_ERROR, SEQUENCE, SEQUENCE_SOFTMAX_CROSS_ENTROPY, SET, SIGMOID_CROSS_ENTROPY, SOFTMAX_CROSS_ENTROPY, TEXT, TIMESERIES, VECTOR
  from ludwig.schema import utils
  from ludwig.schema.metadata import LOSS_METADATA
  from ludwig.schema.utils import ludwig_dataclass
  from ludwig.utils.registry import Registry
label_desc: Use image node_id 3 for calling the CORNLossConfig obj's underlying member method code with example usage: obj.class_similarities_temperature() and returns: int