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    def forward(self,x):
        '''Forward pass, it is equal to PyTorch forward method. Here network computational graph is built
        Parameters:
        x (Tensor): A Tensor containing the input batch of the network
        Returns: 
        An one dimensional Tensor with probability array for each input image
        '''
        x=self.conv_layer_1(x)
        x=self.conv_layer_2(x)
        x=self.dropout1(x)

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    def prepare_data(self):
        '''Prepares datasets. Called once per training execution
        '''
        self.train_loader, self.val_loader, self.test_loader  = self.load_split_train_test()
        
    def train_dataloader(self):
        '''
        Returns:
        (torch.utils.data.DataLoader): Training set data loader
        '''

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    def load_split_train_test(self, valid_size = .2):
        '''Loads data and builds training/validation dataset with provided split size
        
        Parameters:
        valid_size (float): the percentage of data reserved to validation
        Returns:
        (torch.utils.data.DataLoader): Training data loader
        (torch.utils.data.DataLoader): Validation data loader
        (torch.utils.data.DataLoader): Test data loader
        

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def __init__(self, train_data_dir,batch_size=128,test_data_dir=None, num_workers=4):
        '''Constructor method 
        Parameters:
        train_data_dir (string): path of training dataset to be used either for training and validation
        batch_size (int): number of images per batch. Defaults to 128.
        test_data_dir (string): path of testing dataset to be used after training. Optional.
        num_workers (int): number of processes used by data loader. Defaults to 4.
        '''

        # Invoke constructor

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import os
import math
import random as rn
import numpy as np

import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler

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pip install pipenv
pipenv install
pipenv shell
python main.py

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import os
import math
import random as rn
import numpy as np

import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler

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import argparse
import os

# default pytorch import
import torch

# import lightning library
import pytorch_lightning as pl

# import trainer class, which orchestrates our model training

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# MNIST on SageMaker with PyTorch Lightning
import json
import boto3
import sagemaker
from sagemaker.pytorch import PyTorch

# Initializes SageMaker session which holds context data
sagemaker_session = sagemaker.Session()

# The bucket containig our input data

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SM_HOSTS=["algo-1"]
SM_NETWORK_INTERFACE_NAME=eth0
SM_HPS={"batch-size":128,"epochs":6}
SM_USER_ENTRY_POINT=train.py
SM_FRAMEWORK_PARAMS={}
SM_RESOURCE_CONFIG={"current_host":"algo-1","hosts":["algo-1"],"network_interface_name":"eth0"}
SM_INPUT_DATA_CONFIG={"test":{"RecordWrapperType":"None","S3DistributionType":"FullyReplicated","TrainingInputMode":"File"},"train":{"RecordWrapperType":"None","S3DistributionType":"FullyReplicated","TrainingInputMode":"File"}}
SM_OUTPUT_DATA_DIR=/opt/ml/output/data
SM_CHANNELS=["test","train"]
SM_CURRENT_HOST=algo-1