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What is a the Python program you requested given the following:
!mkdir pytorch_trainer_image

%%writefile ./pytorch_trainer_image/train.py

import os 
import subprocess
import datetime
import fire

import torch 
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import pandas as pd 
import numpy as np
from sklearn.preprocessing import StandardScaler

class TrainData(Dataset):
    def __init__(self, X_data, y_data):
        self.X_data = X_data
        self.y_data = y_data
        
    def __getitem__(self, index):
        return self.X_data[index], self.y_data[index]
        
    def __len__ (self):
        return len(self.X_data)
    
class BinaryClassifier(nn.Module):
    def __init__(self):
        super(BinaryClassifier, self).__init__()
        # 27 input features
        self.h1 = nn.Linear(27, 64) 
        self.h2 = nn.Linear(64, 64)
        self.output_layer = nn.Linear(64, 1) 
        
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(p=0.1)
        self.batchnorm1 = nn.BatchNorm1d(64)
        self.batchnorm2 = nn.BatchNorm1d(64)
        
    def forward(self, inputs):
        x = self.relu(self.h1(inputs))
        x = self.batchnorm1(x)
        x = self.relu(self.h2(x))
        x = self.batchnorm2(x)
        x = self.dropout(x)
        x = self.output_layer(x)
        
        return x

def binary_acc(y_pred, y_true):
    """Calculates accuracy"""
    y_pred_tag = torch.round(torch.sigmoid(y_pred))

    correct_results_sum = (y_pred_tag == y_true).sum().float()
    acc = correct_results_sum/y_true.shape[0]
    acc = torch.round(acc * 100)
    
    return acc

def train_evaluate(training_dataset_path, validation_dataset_path, batch_size, num_epochs, output_dir):
    
    batch_size = int(batch_size)
    num_epochs = int(num_epochs)
    
    # Read in train/validation data and concat 
    df_train = pd.read_csv(training_dataset_path)
    df_validation = pd.read_csv(validation_dataset_path)
    df = pd.concat([df_train, df_validation])

    categorical_features = ['workclass', 'occupation']
    target='income_bracket'

    #...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Create PyTorch Training Script
Two down, two to go! Now we will develop a PyTorch training script. We will use Pandas DataFrames combined with PyTorch's Dataset and Dataloader to ingest the data from CSVs, build a model with torch.nn, and write a training loop to train this model.