class_docstr: MiniBatchKMeans.
  
  K-Means clustering algorithm with mini batch.
class_docstr_tok: ["MiniBatchKMeans",".","K-Means","clustering","algorithm","with","mini","batch","."]
code: def __init__(
      self,
      n_clusters,
      max_iter=300,
      init=c_ops.RANDOM_INIT,
      distance=c_ops.SQUARED_EUCLIDEAN_DISTANCE,
      metric=None,
      num_features=None,
      log_dir="/tmp/tflearn_logs/",
      global_step=None,
      session=None,
      graph=None,
      name=None,
  ):
      super(MiniBatchKMeans, self).__init__(
          n_clusters,
          max_iter=max_iter,
          init=init,
          distance=distance,
          metric=metric,
          num_features=num_features,
          log_dir=log_dir,
          global_step=global_step,
          session=session,
          graph=graph,
          name=name,
      )
  
      self.use_mini_batch = True
  
imports: from __future__ import division, print_function, absolute_import
  from datetime import datetime
  import os
  import math
  import numpy
  import time
  import tensorflow.compat.v1
  from tensorflow.contrib.factorization.python.ops import clustering_ops
  from tensorflow.contrib.tensor_forest.python.ops import data_ops
  from tensorflow.python.ops import state_ops, array_ops, math_ops
  from ...utils import validate_dim, read_tensor_in_checkpoint, prepare_X
  from ...data_utils import get_num_features, get_num_sample
  from ...data_flow import generate_data_tensor
  from ...distances import euclidean, cosine
  from ..base import BaseEstimator
label_desc: Use image node_id 1 to create a new MiniBatchKMeans object from inherited base classes: KMeans with example: obj = MiniBatchKMeans(n_clusters, max_iter, init, distance, metric, num_features, log_dir, global_step, session, graph, name)