Implement function for BERT quantization tutorial, resolves issue #1971…

… (#2403)

Co-authored-by: Carl Parker <carljparker@meta.com>

intermediate_source/dynamic_quantization_bert_tutorial.rst

@@ -255,6 +255,9 @@ model before and after the dynamic quantization.
         torch.manual_seed(seed)
     set_seed(42)
 
+    # Initialize a global random number generator
+    global_rng = random.Random()
+
 
 2.2 Load the fine-tuned BERT model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -525,6 +528,21 @@ We can serialize and save the quantized model for the future use using
 
 .. code:: python
 
+    def ids_tensor(shape, vocab_size, rng=None, name=None):
+        #  Creates a random int32 tensor of the shape within the vocab size
+        if rng is None:
+            rng = global_rng
+
+        total_dims = 1
+        for dim in shape:
+            total_dims *= dim
+
+        values = []
+        for _ in range(total_dims):
+            values.append(rng.randint(0, vocab_size - 1))
+
+        return torch.tensor(data=values, dtype=torch.long, device='cpu').view(shape).contiguous()
+
     input_ids = ids_tensor([8, 128], 2)
     token_type_ids = ids_tensor([8, 128], 2)
     attention_mask = ids_tensor([8, 128], vocab_size=2)