class_docstr: Abstract Classification Model
class_docstr_tok: ["Abstract","Classification","Model"]
docstr: Args:
      embedding: embedding object
      sequence_length: target sequence length
      hyper_parameters: hyper_parameters to overwrite
      multi_label: is multi-label classification
      text_processor: text processor
      label_processor: label processor
docstr_tok: ["Args",":","embedding",":","embedding","object","sequence_length",":","target","sequence","length","hyper_parameters",":","hyper_parameters","to","overwrite","multi_label",":","is","multi-label","classification","text_processor",":","text","processor","label_processor",":","label","processor"]
code: def __init__(
      self,
      embedding: ABCEmbedding = None,
      *,
      sequence_length: int = None,
      hyper_parameters: Dict[str, Dict[str, Any]] = None,
      multi_label: bool = False,
      text_processor: ABCProcessor = None,
      label_processor: ABCProcessor = None,
  ):
      """
  
      Args:
          embedding: embedding object
          sequence_length: target sequence length
          hyper_parameters: hyper_parameters to overwrite
          multi_label: is multi-label classification
          text_processor: text processor
          label_processor: label processor
      """
      super(ABCClassificationModel, self).__init__()
      if embedding is None:
          embedding = BareEmbedding()  # type: ignore
  
      if hyper_parameters is None:
          hyper_parameters = self.default_hyper_parameters()
  
      if text_processor is None:
          text_processor = SequenceProcessor()
  
      if label_processor is None:
          label_processor = ClassificationProcessor(
              multi_label=multi_label
          )
  
      self.tf_model: keras.Model = None
      self.embedding = embedding
      self.hyper_parameters = hyper_parameters
      self.sequence_length = sequence_length
      self.multi_label = multi_label
  
      self.text_processor = text_processor
      self.label_processor = label_processor
  
imports: import random
  from abc import ABC
  import numpy
  from typing import List, Dict, Any, Union
  from sklearn import metrics
  from tensorflow import keras
  import kashgari
  from kashgari.embeddings import ABCEmbedding, BareEmbedding
  from kashgari.generators import BatchDataSet, CorpusGenerator
  from kashgari.layers import L
  from kashgari.logger import logger
  from kashgari.metrics.multi_label_classification import multi_label_classification_report
  from kashgari.processors import ABCProcessor
  from kashgari.processors import ClassificationProcessor
  from kashgari.processors import SequenceProcessor
  from kashgari.tasks.abs_task_model import ABCTaskModel
  from kashgari.types import TextSamplesVar, ClassificationLabelVar, MultiLabelClassificationLabelVar
label_desc: Use image node_id 2 to create a new ABCClassificationModel object from inherited base classes: ABCTaskModel, ABC with example: obj = ABCClassificationModel(embedding)