Hi , Many thnaks fo ryour reply. muy model is 
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import TensorDataset, DataLoader
import torch.optim as optim
import torch.nn as nn
from torch.utils.data.dataset import random_split
from torch.nn import functional as F
import matplotlib.pyplot as plt
from torch.autograd import Variable
class ConvNet(nn.Module):
def init(self,numf1,numf2,fz1,fz2,nn2,nn3):
super(ConvNet, self).init()
self.numf1=numf1
self.numf2=numf2
self.fz1=fz1
self.fz2=fz2
self.nn2=nn2
self.nn3=nn3
self.layer1 = nn.Sequential(nn.Conv3d(1, self.numf1, kernel_size=self.fz1, stride=1, padding=2),nn.ReLU(),nn.MaxPool3d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(nn.Conv3d(32,self.numf2, kernel_size=self.fz2, stride=1, padding=2),nn.ReLU(),nn.MaxPool3d(kernel_size=2, stride=2))
self.fc1 = nn.Linear(3072, self.nn2) ##3027
self.drop_out = nn.Dropout(0.3)
print(“Dropout”)
self.fc2 = nn.Linear( self.nn2, self.nn3) # FULLY CONNECTED LAYERS
self.fc3 = nn.Linear( self.nn3, 1) # FULLY CONNECTED LAYERS
    self.sigmoid = nn.Sigmoid()
    
def forward(self, x):

print(“here: {}”.format(x.shape))
   # print(type(x))

x=np.expand_dims(x,axis=0)
print(“xthen”,x.shape)
x=x.astype(int)
x=torch.from_numpy(x)
    x=x.unsqueeze(1).float()

print(type(x))
    #print(x.shape)
    out = self.layer1(x)
    
   # print(out.shape)
  #  print(out)
    out = self.layer2(out)

print(out.shape)
    out = out.view(out.size(0), -1)

print(out.shape)
    out = self.fc1(out)
    out = self.fc2(out)
    out = self.fc3(out)
    out = self.sigmoid(out)

print(“outsize”,out.shape)
    return out