Sharma96

<div id="accordion" role="tablist" aria-multiselectable="true">
  <div class="card">
    <div class="card-header" role="tab" id="headingOne">
      <h5 class="mb-0">
        <a data-toggle="collapse" data-parent="#accordion" href="#collapseOne" aria-expanded="true" aria-controls="collapseOne">
          Collapsible Group Item #1
        </a>
      </h5>
    </div>

Sharma96

class Intermediate_table(models.Model):
    university = models.CharField(max_length=255);
    branch = models.CharField(max_length=255);
    sem = models.CharField(max_length=255);
    subject = models.CharField(max_length=255);
    year = models.CharField(max_length=255);
    user_id = models.CharField(max_length=255);
    old_url = models.CharField(max_length=255);
    new_url = models.CharField(max_length=255);
    status = models.BooleanField(max_length=255);

Sharma96

void fun(int n){
  if(n == 0)
    return;
  if(n>=1&&n<=5){
    fun(n-1);
  }
  else{
    fun(n/2);
  }
}

Sharma96

//consider dp[] to be a global array
int	minCoins(int	N,	int	M)
{		
        if(dp[N] !=	-1)	
            return	dp[N];							
        if	(N	==	0)
            return	0;							
        int	res	=	INF;	
        for(int i=0;i<M;i++){					
            if(coins[i]<=N){		

Sharma96

#include <iostream>
#include <queue>
#include <stdio.h>
#include <stdlib.h>
#include <utility>
#include <cstdio>
#define n 1001
using namespace std;
///char arr[1001][1001];

Sharma96

#include<iostream>
#include<string>
#include<utility>
#include<unordered_map>
#include<map>
using namespace std;

bool is_per(string s, string s1) {
	if (s.length() != s1.length()) {
		//cout << "sdfg";

Sharma96

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

from collections import defaultdict
from io import StringIO

Sharma96

TypeError                                 Traceback (most recent call last)
<ipython-input-10-394d9d79c99b> in <module>()
     67 # training pipeline
     68 lr = 0.001
---> 69 logits = my_net(x, n_classes=n_classes)
     70 cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y)
     71 loss_function = tf.reduce_mean(cross_entropy)

<ipython-input-10-394d9d79c99b> in my_net(x, n_classes)
     42     drop2 = tf.nn.dropout(pool2, keep_prob=keep_prob)