class_docstr: Creates a sequence of labeled points from provided numpy arrays.
class_docstr_tok: ["Creates","a","sequence","of","labeled","points","from","provided","numpy","arrays","."]
docstr: Initializes the sequence.
  
  Args:
    features: Numpy `array` of features, indexed by the first dimension.
    labels: Numpy `array` of features, with the same first dimension as
            `features`.
    batch_size: Integer, number of elements in each training batch.
docstr_tok: ["Initializes","the","sequence",".","Args",":","features",":","Numpy","`","array","`","of","features",",","indexed","by","the","first","dimension",".","labels",":","Numpy","`","array","`","of","features",",","with","the","same","first","dimension","as","`","features","`",".","batch_size",":","Integer",",","number","of","elements","in","each","training","batch","."]
code: def __init__(self, features, labels, batch_size):
      """Initializes the sequence.
  
      Args:
        features: Numpy `array` of features, indexed by the first dimension.
        labels: Numpy `array` of features, with the same first dimension as
                `features`.
        batch_size: Integer, number of elements in each training batch.
      """
      self.features = features
      self.labels = labels
      self.batch_size = batch_size
  
file_path: probability/tensorflow_probability/examples/logistic_regression.py
imports: import os
  from absl import app
  from absl import flags
  from matplotlib import cm
  from matplotlib import figure
  from matplotlib.backends import backend_agg
  import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 1 to create a new ToyDataSequence object from inherited base classes: tf_keras.utils with example: obj = ToyDataSequence(features, labels, batch_size)