A company wants to group customer records into clusters based on two numerical behavioral features. Since the records are not labeled, use K-Means clustering to create the groups. Generate a synthetic dataset using scikit-learn and group its data points using the number of clusters provided as input.

Dataset Generation: Generate the dataset using: from sklearn.datasets import make_blobs

Use the following fixed parameters:

• n_samples=300
• centers=4
• n_features=2
• random_state=0

Task:

• Write a Python program that performs the following steps:

• Generate the synthetic dataset using the specified parameters.

• Read two integers from standard input:

• n_clusters

• random_state

• Initialize a K-Means model using:

• n_clusters equal to the input value

• random_state equal to the input value

• n_init=10

• Train the K-Means model using the generated dataset.

• Count the number of data points in every cluster.

• Sort the cluster sizes in ascending order.

• Print each sorted cluster size on a separate line.

Constraints: 2 <= n_clusters <= 6 0 <= random_state <= 10000

Input Format: Two space-separated integers: <n_clusters><space><random_state>

Output Format: Print n_clusters lines. Each line must contain one cluster size. The sizes must be printed in ascending order.

Sample Input 1:

4 0

Sample Output 1:

74
74
75
77

Note: Cluster label numbers can vary internally. Therefore, print only the sorted cluster sizes. Do not print any extra text or labels.