Remove unneeded print from debugging.

MICROLIB.md

@@ -46,6 +46,22 @@ wrapped = Wrapped(dataset, wrapper_fn=torch.tensor)
 print(wrapped[14])
 ```
 
+Before the `wrapper_fn` is called, a copy of the sample is made to avoid warnings 
+about non-writeable underlying tensor. If you are going to only read from the sample,
+you can pass `copy_before_wrapper_fn=False` when initializing the object. 
+For example:
+
+```python
+import numpy as np
+import torch
+from mmap_ninja.base import Wrapped
+
+dataset = [np.array([1, 2, 3], np.array([-1, 10]))]
+wrapped = Wrapped(dataset, wrapper_fn=torch.tensor, copy_before_wrapper_fn=False)
+
+print(wrapped[14])
+```
+
 ### Create a Numpy memmap from a Numpy array
 
 The `mmap_ninja.numpy` module provides utilities for
@@ -179,6 +195,19 @@ images = RaggedMmap('val_images', wrapper_fn=torch.tensor)
 assert isinstance(torch.Tensor, images[4])
 ```
 
+Before the `wrapper_fn` is called, a copy of the sample is made to avoid warnings 
+about non-writeable underlying tensor. If you are going to only read from the sample,
+you can pass `copy_before_wrapper_fn=False` when initializing the object. 
+
+```python
+import numpy as np
+import torch
+from mmap_ninja.ragged import RaggedMmap
+
+images = RaggedMmap('val_images', wrapper_fn=torch.tensor, copy_before_wrapper_fn=False)
+assert isinstance(torch.Tensor, images[4])
+```
+
 ### Append new samples to a RaggedMmap
 
 To append a single sample, use `RaggedMmap.append`.

python/mmap_ninja/base.py

@@ -239,8 +239,6 @@ def __getitem__(self, item):
         sample = self.data[item]
         if self.copy_before_wrapper_fn:
             sample = copy(sample)
-        else:
-            print("Yo")
         return self.wrapper_fn(sample)
 
     def __len__(self):