class_docstr: Synthetic data generator for Mixture of Gaussians (MoG).
  
  Attributes:
    partition_generator: A partition generator with a `generate_batch` method
      for producing the cluster partitions, or `None`. `generate_batch(n,
      batch_size)` should return a list of `np.ndarray` representing a batch of
      partitions, where each array is a sampled partition configuration, and the
      k-th element in the array is the size of the k-th partition. If set to
      `None`, the `generate_by_partitions` method can be used to create MOG with
      an arbitrary partition configuration.
    x_dim: the number of dimensions.
    prior_sigma: the standard deviation for generating the centers of the
      mixture components.
    sigma_min: the minimum standard deviation for each mixture component.
    sigma_max: the maximum standard deviation for each mixture component.
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docstr: Generates a batch of Gaussian mixtures using the partition_generator.
  
  Arguments:
    n: The number of data points.
    batch_size: Batch size.
    standardize: Boolean of whether to standardize the data.
  
  Returns:
    data: A `np.ndarray` of shape `[batch_size, n_points, x_dim]`.
    labels: An int `np.ndarray` of shape `[batch_size, n_points]`.
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code: def generate_batch(
      self, n: int, batch_size: int, standardize: bool = True
  ) -> Tuple[np.ndarray, np.ndarray]:
      """Generates a batch of Gaussian mixtures using the partition_generator.
  
      Arguments:
        n: The number of data points.
        batch_size: Batch size.
        standardize: Boolean of whether to standardize the data.
  
      Returns:
        data: A `np.ndarray` of shape `[batch_size, n_points, x_dim]`.
        labels: An int `np.ndarray` of shape `[batch_size, n_points]`.
      """
      if self.partition_generator is None:
          raise ValueError(
              "partition_generator must be set in the constructor."
          )
  
      partitions = self.partition_generator.generate_batch(
          n=n, batch_size=batch_size
      )
  
      return self.generate_by_partitions(
          partitions=partitions, standardize=standardize
      )
  
imports: from typing import List, Tuple
  import numpy
label_desc: Use image node_id 2 for calling the MOGGenerator obj's underlying member method code with example usage: obj.generate_batch(n, batch_size, standardize) and returns: self