class_docstr: MiniBatchKMeans.
  
  K-Means clustering algorithm with mini batch.
class_docstr_tok: ["MiniBatchKMeans",".","K-Means","clustering","algorithm","with","mini","batch","."]
docstr: fit.
  
  Compute the K-Means clustering for the input data.
  
  Arguments:
      X: `Array` or `list` of `Array` of shape (n_samples, n_features).
          The training data.
      shuffle: `bool`. If True, data are shuffled.
      batch_size: `int`. The batch size.
      display_step: `int`. The step to display training information.
      n_jobs: `int`. The number of jobs to use for the computation.
      max_steps: `int`. Maximum number of optimization steps to run.
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code: def fit(
      self,
      X,
      batch_size=1024,
      shuffle=True,
      display_step=500,
      n_jobs=1,
      max_steps=None,
  ):
      """fit.
  
      Compute the K-Means clustering for the input data.
  
      Arguments:
          X: `Array` or `list` of `Array` of shape (n_samples, n_features).
              The training data.
          shuffle: `bool`. If True, data are shuffled.
          batch_size: `int`. The batch size.
          display_step: `int`. The step to display training information.
          n_jobs: `int`. The number of jobs to use for the computation.
          max_steps: `int`. Maximum number of optimization steps to run.
  
      """
      super(KMeans, self).fit(
          X,
          shuffle=shuffle,
          display_step=display_step,
          n_jobs=n_jobs,
          max_steps=max_steps,
          batch_size=batch_size,
      )
  
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 2 for calling the MiniBatchKMeans obj's underlying member method code with example usage: obj.fit(X, batch_size, shuffle, display_step, n_jobs, max_steps) without return types