DFoly

from typing import List, Dict, Tuple

x: int = 10
y: float = 0.8
string: str = 'I am a string'
hash_map: Dict[str, int] = {}
List1: List[int] = []
my_tuple: Tuple[str, int] = ()
    

DFoly

import numpy as np
import pandas as pd
from sklearn.datasets import make_blobs
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression


class CustomImputer(BaseEstimator, TransformerMixin):

DFoly

import numpy as np
import pandas as pd
from typing import Dict, List
from sklearn.datasets import make_blobs
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

DFoly

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from google.cloud import bigquery
import re
import logging
import sys

PROJECT='user-logs-237110'
schema = 'remote_addr:STRING, timelocal:STRING, request_type:STRING, status:STRING, body_bytes_sent:STRING, http_referer:STRING, http_user_agent:STRING'

DFoly

from apache_beam.options.pipeline_options import PipelineOptions
from google.cloud import pubsub_v1
from google.cloud import bigquery
import apache_beam as beam
import logging
import argparse
import sys
import re

DFoly

from faker import Faker
import time
import random
import os
import numpy as np
from datetime import datetime, timedelta



LINE = """\

DFoly

from stream_logs import generate_log_line
import logging
from google.cloud import pubsub_v1
import random
import time


PROJECT_ID="user-logs-237110"
TOPIC = "userlogs"

DFoly

     def predict(self, X):
        """Returns predicted labels using Bayes Rule to
        Calculate the posterior distribution
        
        Parameters:
        -------------
        X: N*d numpy array
        
        Returns:
        ----------

DFoly

      def fit(self, X):
        """Compute the E-step and M-step and
            Calculates the lowerbound
        
        Parameters:
        -----------
        X: (N x d), data 
        
        Returns:
        ----------

DFoly

def _compute_loss_function(self, X, pi, mu, sigma):
    """Computes lower bound loss function
        
    Parameters:
    -----------
    X: (N x d), data 
        
    Returns:
    ---------
    pi: (C)