Constant difference while transforming forecast from its "differenced" form

Question

I am transforming "differencing" transformation to my data. But when i want to do the inverse operation to my forecast, I am getting something like this, this, and this as prediction. How can I fix this "constant blank" issue?

How I apply difference transform to my dataset (pretty simple.):

df['diff'] = df.loc[:,'RequestResponseLogDuration'].diff(1)

And here is how I am trying to revert this operation:

def inverse_difference(history, yhat, interval=1):
    return yhat + history[-interval]


for x, y in train_data_multi.take(10):
     predictions = list()
     for i in range(len(y)):
       # predict
       X, T = y[i, 0:-1], y[i, -1]
       yhat = multi_step_model.predict(x)[i]
   
    
       # invert differencing
       yhat = inverse_difference(dataset, yhat, len(T)+1-i)
       
       # store forecast
       predictions.append(yhat)
     multi_step_plot(x[v], y[v], predictions[v])

UPDATE I transferred code to this:

from tensorflow.python.ops.numpy_ops import np_config
np_config.enable_numpy_behavior()
for x, y in train_data_multi.take(1):
     predictions = list()
     for i in range(len(y)):
       # predict
      
      
       yhat = multi_step_model.predict(x)[i]
   
       # invert scaling
       # invert differencing
       yhat = inverse_difference(dataset, yhat, i)
       xx=True


       # store forecast
       predictions.append(yhat)
     multi_step_plot(x[v], y[v], predictions[v])
     print(predictions[v].ravel()-y[v].ravel())

Now this is the result