I am facing this error:
python classfn3daj3.py
118060
tensor([[6.0167e+05, 4.4948e+06, 3.9800e+00,  ..., 0.0000e+00, 0.0000e+00,
         0.0000e+00],
        [6.0591e+05, 4.4951e+06, 1.2120e+01,  ..., 0.0000e+00, 0.0000e+00,
         0.0000e+00],
        [5.9716e+05, 4.4925e+06, 5.9400e+00,  ..., 0.0000e+00, 0.0000e+00,
         0.0000e+00],
        ...,
        [6.0279e+05, 4.5158e+06, 2.0560e+01,  ..., 0.0000e+00, 0.0000e+00,
         0.0000e+00],
        [6.0283e+05, 4.5159e+06, 2.5710e+01,  ..., 0.0000e+00, 0.0000e+00,
         0.0000e+00],
        [5.8590e+05, 4.5146e+06, 2.6260e+01,  ..., 0.0000e+00, 0.0000e+00,
         0.0000e+00]])
torch.Size([118060, 7673])
-tensor label points**********************************-
tensor([0, 1, 0,  ..., 0, 0, 0])
size of tensor label torch.Size([118060])
**--------------------------------------
<torch.utils.data.dataloader.DataLoader object at 0x7f6d1da22c18>
--------------------------------------
/home/raman/anaconda2/envs/pyto3/lib/python3.6/site-packages/torch/cuda/__init__.py:116: UserWarning: 
    Found GPU1 NVS 315 which is of cuda capability 2.1.
    PyTorch no longer supports this GPU because it is too old.
    
  warnings.warn(old_gpu_warn % (d, name, major, capability[1]))
NeuralNet(
  (fc1): Linear(in_features=118060, out_features=10, bias=True)
  (relu): ReLU()
  (fc2): Linear(in_features=10, out_features=3, bias=True)
)
Traceback (most recent call last):
  File "classfn3daj3.py", line 117, in <module>
    outputs = model(dpoints).to(device)
  File "/home/raman/anaconda2/envs/pyto3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/raman/recon/nn_3d.py", line 14, in forward
    out = self.fc1(x)
  File "/home/raman/anaconda2/envs/pyto3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/raman/anaconda2/envs/pyto3/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 55, in forward
    return F.linear(input, self.weight, self.bias)
  File "/home/raman/anaconda2/envs/pyto3/lib/python3.6/site-packages/torch/nn/functional.py", line 1026, in linear
    output = input.matmul(weight.t())
RuntimeError: Expected object of type torch.cuda.LongTensor but found type torch.cuda.FloatTensor for argument #2 'mat2'

My code is the following:
input_size = 7673
hidden_size = 10
num_classes = 3
num_epochs = 5
batch_size = 1
learning_rate = 0.01
torch.cuda.device(0)
def np_fill(data):
    lens = [len(data[i]) for i in range(0,len(data))]
    max_len = np.asarray(lens).max()
    # print(lens)

    for k in range(0,len(data)):
        num_append = max_len - len(data[k])
        to_append = np.ndarray.tolist(np.zeros(num_append))
        data[k].extend(to_append)
    return (data,max_len)


def pad(A, length):
    arr = np.zeros(length)
    arr[:len(A)] = A
    return arr

def to_tensor(data1,label1):
    
    points = data1.values
    labels = label1.values
    
    points=np.delete(points,(9989,16027,16688,18957,43657,48915,50115,57132, 71211,73804,93884,104406,116125),0);
    labels=np.delete(labels,(9989,16027,16688,18957,43657,48915,50115,57132, 71211,73804,93884,104406,116125),0);

    points_list = []

    for kk in range(0,points.shape[0]):
        points[kk] = points[kk].replace(',',' ')
        points[kk] = np.core.defchararray.split(points[kk]," ")
        points_list.append(np.ndarray.tolist(points[kk]))
    print(len(points_list))
    fpoints,maxl =  np_fill(points_list) 
    p_len = len(points_list)
    max_len = maxl
    fpoints =  np.asarray(fpoints)
    arpoints=fpoints.astype('float64')
    ten_p = []
    l = []
    ten_p = torch.from_numpy(arpoints).float()
    print(ten_p)
    l = ten_p.size()
    print(l)
    #ten_l = torch.from_numpy(labels).float()
    ten_l = torch.from_numpy(labels)
    print("-tensor label points**********************************-")
    print(ten_l)
    print("size of tensor label", ten_l.size())
    return ten_p,ten_l

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

class BDatasetFromCSV(Dataset):

    def __init__(self,csv_path):
        self.data = pd.read_csv(csv_path)
        data_labels = self.data.iloc[:, 0]
        data_points = self.data.iloc[:, 1]
        tens,lb = to_tensor(data_points,data_labels)
        self.points = tens
        self.labels = lb
        
    def __getitem__(self, index):
        d_labels = self.labels[index]
        d_points = self.points[index]
        return d_points, d_labels  
   
    def __len__(self):
        return len(self.data.index)

if __name__ == "__main__":
    #Load the training set
	custom_b_from_csv = BDatasetFromCSV('broof.csv')  
	#Create a loader for the training set
	b_dataset_loader = torch.utils.data.DataLoader(dataset= custom_b_from_csv,batch_size=10,shuffle=False)
	#total_step = len(b_dataset_loader)
	print("**--------------------------------------")
	print(b_dataset_loader)
	#print(total_step)
	print("--------------------------------------")
	#model = NeuralNet(input_size, hidden_size, num_classes)
	model = NeuralNet(input_size, hidden_size, num_classes).to(device)
	print(model)
	criterion = torch.nn.CrossEntropyLoss()
	#criterion = torch.nn.MSELoss()
	optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
	#optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) 	
	for i, (d_labels,d_points) in enumerate(b_dataset_loader):
	   
	    dpoints = d_points.to(device)
	    #dlabels = Variable(d_labels).to(device)
	    dlabels = d_labels.to(device)
	    # Clear gradients
	    optimizer.zero_grad()
	    # Forward pass
	    outputs = model(dpoints).to(device)
        # Calculate loss
	    loss = criterion(outputs, dlabels)
	    # Backward pass
	    loss.backward()
	    # Update weights
	    optimizer.step()
	    break

Could you tell me the major mistake i have done inthis and hepl me to rectify it.