docstr: Parameters
  ----------
  data_set : Dataset
      A dataset for which each instance is used to compute its embedding vector.
docstr_tok: ["Parameters","--","--","--","--","--","data_set",":","Dataset","A","dataset","for","which","each","instance","is","used","to","compute","its","embedding","vector","."]
code: def embed(self, data_set):
      """
      Parameters
      ----------
      data_set : Dataset
          A dataset for which each instance is used to compute its embedding vector.
      """
      pass
  
imports: from abc import ABC, abstractmethod
  import numpy
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.preprocessing import normalize
  from sklearn.svm import LinearSVC
  from sklearn.utils.multiclass import is_multilabel
  from small_text.utils.classification import empty_result, prediction_result
  from small_text.utils.data import check_training_data
label_desc: Use image node_id 1 for calling the EmbeddingMixin obj's underlying member method code with example usage: obj.embed(data_set) without return types