thanks but still the same error,
this is my model : i want that the output of the DCT is the input of the first autoencoder and the out put of this autoencoder is the input of the second autoencoder
if name==‘main’:
Load the training and testing dataset separately by calling the function for each of their root folder locations
X_train, y_train = load_data(root_folder_train)
X_test, y_test = load_data(root_folder_test)
print(X_train.shape, y_train.shape)
print(X_test.shape, y_test.shape)

Creating the model for EEG DATA:
1. DCT Application
tf_DCT=[]
N_samples = 256

for i in X_train:
    tf_DCT_train = DCT_funtion(i,N_samples)
    tf_DCT.append(tf_DCT_train)
input=torch.from_numpy(tf_DCT)
#print(tf_DCT_train.size)
#tf_DCT_test= DCT_funtionn(X_test,N_samples)
print("successful step")

#Model:
#Application of autoencoder:
model=AE()
classifiier= torch.nn.Softmax(model)
optimizer=optim.Adam(classifiier.parameters(),lr=1e-3)
criterion =torch.nn.MSELoss()