try checking the files exist in the directory
import torch
import torch.nn.functional as F
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.backends.cudnn as cudnn
import numpy as np
import torch.nn as nn
import os

class LeNet_5_Caffe(nn.Module):
    """LeNet-5 without padding in the first layer.
    This is based on Caffe's implementation of Lenet-5 and is slightly different
    from the vanilla LeNet-5. Note that the first layer does NOT have padding
    and therefore intermediate shapes do not match the official LeNet-5.

    Based on https://github.com/mi-lad/snip/blob/master/train.py
    by Milad Alizadeh.
    """

    def __init__(self, save_features=None, bench_model=False):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 10, 5, padding=0, bias=True)
        self.conv2 = nn.Conv2d(10, 20, 5, bias=True)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.fc1(x.view(-1, 320)))
        x = F.log_softmax(self.fc2(x), dim=1)


        return x

model_10=LeNet_5_Caffe()

torch.save(model_10.state_dict(), 'abc.pt')
print(os.getcwd())
print(os.listdir())
model_10.load_state_dict(torch.load('abc.pt'))

output of print statement is
/content ['.config', 'abc.pt', 'sample_data']

<All keys matched successfully>
``