here is my code
import torch.nn as nn
import csv
import numpy
import numpy as np
class PrintLayer(nn.Module):
    def __init__(self):
        super(PrintLayer, self).__init__()
        m = nn.Conv2d(3, 96, kernel_size=(11,11), stride=(4,4), padding=0, bias = False)
    
    def forward(self, x):
        # Do your print / debug stuff here
        #print("x is",x)
        return x

def init_weights(m):
      if type(m) == nn.Conv2d:
        torch.nn.init.xavier_uniform(m.weight)
        #for weight
        a_file1 = open("1st_layer_8bit_weight.txt","r")
        list_of_lists1 = []
        for line in a_file1:
          stripped_line1 = line.strip()
          line_list1 = stripped_line1.split()
          list_of_lists1.extend(line_list1)
        a_file1.close()

        b = torch.from_numpy(numpy.array(list_of_lists1,dtype= 'int8'))
        w = torch.tensor(b)
        ten = w.reshape(96,3,11,11)
        m.weight.data = ten

        #for bias
        """a_file12 = open("bias.txt","r")
        list_of_lists12 = []
        for line in a_file12:
          stripped_line12 = line.strip()
          line_list12 = stripped_line12.split()
          list_of_lists12.extend(line_list12)
        a_file12.close()

        c = torch.from_numpy(numpy.array(list_of_lists12,dtype= 'float32'))
        d = torch.tensor(c)
        ten1 = d.reshape(256)
        m.bias.data = ten1"""
       
        #print("actual weight is",m.weight)
        #print(m.bias)
      
model = nn.Sequential(
        nn.Conv2d(3, 96, kernel_size=(11,11), stride=(4,4), padding=0, bias = False),
        PrintLayer(),
         # Add Print layer for debug
        )
model.apply(init_weights)

#for input
a_file = open("1st_layer_8bit_input.txt","r")
list_of_lists = []
for line in a_file:
  stripped_line = line.strip()
  line_list = stripped_line.split()
  list_of_lists.extend(line_list)
a_file.close()
b = torch.from_numpy(numpy.array(list_of_lists,dtype= 'int8'))
x = torch.tensor(b)
x = x.reshape(1,3,227,227)
output = model(x)

np.set_printoptions(threshold=50000000,formatter={'float_kind':'{:f}'.format})
output = output.detach().numpy()
file = open("correct_output.txt","w")
file.write(str(output))
file.close()
print(model)
#for weight and bias
print(list(model.named_parameters()))
#print(model.state_dict())```