making unconditional generation work

The unconditional generation works now but if the seed is fixed, the sample is the same every time.
n_samples > 1 will give different samples though.
I am giving the start token as '<|endoftext|>' for the unconditional generation.

examples/run_gpt2.py

@@ -106,6 +106,23 @@ def run_model():
                 print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
                 print(text)
         print("=" * 80)
+    if args.unconditional:
+        generated = 0
+        for _ in range(args.nsamples // args.batch_size):
+            out = sample_sequence(
+                model=model, length=args.length,
+                context=None,
+                start_token=enc.encoder['<|endoftext|>'],
+                batch_size=args.batch_size,
+                temperature=args.temperature, top_k=args.top_k, device=device
+            )
+            out = out[:,1:].tolist()
+            for i in range(args.batch_size):
+                generated += 1
+                text = enc.decode(out[i])
+                print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
+                print(text)
+        print("=" * 80)
 
 if __name__ == '__main__':
     run_model()