Merge branch 'main' into main

Author: Svetlana Karslioglu

beginner_source/data_loading_tutorial.py

@@ -165,9 +165,7 @@ def __getitem__(self, idx):
 
 fig = plt.figure()
 
-for i in range(len(face_dataset)):
-    sample = face_dataset[i]
-
+for i, sample in enumerate(face_dataset):
     print(i, sample['image'].shape, sample['landmarks'].shape)
 
     ax = plt.subplot(1, 4, i + 1)
@@ -356,9 +354,7 @@ def __call__(self, sample):
                                                ToTensor()
                                            ]))
 
-for i in range(len(transformed_dataset)):
-    sample = transformed_dataset[i]
-
+for i, sample in enumerate(transformed_dataset):
     print(i, sample['image'].size(), sample['landmarks'].size())
 
     if i == 3:

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)