<｜begin▁of▁sentence｜>### Instruction:
In the context of a quality assurance management scenario, let's imagine we're tasked with enhancing the quality control processes within a manufacturing setup. Here, we deal with a vast amount of data points, each representing a multidimensional profile of the items coming off the production line. These profiles could include dimensions such as weight, size, color consistency, and more, all of which are critical for ensuring the final product meets our stringent quality standards.

Our objective is to refine our quality control by implementing a sophisticated review mechanism that categorizes these multidimensional data points into distinct clusters, where each cluster represents items with similar characteristics. This categorization aids in identifying and addressing specific quality issues. To achieve this, we're considering the adoption of a top-down, or divisive, clustering approach that leverages Gaussian mixture models. This technique will enable us to partition the data recursively, breaking down the entire dataset into increasingly specific groups until we achieve a satisfactory level of detail.

The crux of our inquiry revolves around how to effectively apply this divisive clustering algorithm, which utilizes Gaussian mixture models, to systematically segment the dataset. Additionally, a crucial aspect of this process is determining the manner in which the final cluster labels are allocated to each data point, ensuring that each item is accurately classified according to its defining characteristics.

Let's translate this challenge into a concrete operational question: How can we apply a divisive clustering algorithm, based on Gaussian mixture models, to methodically divide our multidimensional quality control data into well-defined clusters? Furthermore, what is the most efficient way to accurately assign the resulting cluster labels to each item in our dataset, thereby enhancing our quality control measures through precise identification and categorization of product characteristics?

This refined approach will not only bolster our quality assurance framework but will also provide invaluable insights into the specific areas of the production process that may require adjustments, thereby upholding the high standards of quality and excellence we are committed to.
### Response:
from graspologic.cluster import DivisiveCluster
import numpy as np

# Simulate some data to cluster
np.random.seed(1)
X = np.random.randn(100, 2)

# Initialize the DivisiveCluster with GMM as the underlying clustering method
# and fit to the data X
cluster = DivisiveCluster(cluster_method='gmm', max_components=5)
cluster.fit(X)

# Predict cluster labels for X
labels = cluster.predict(X)

# Print the predicted cluster labels
print(labels)
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