Updated logger warnings and function docstring

Signed-off-by: Ashvin Kumar <quic_ashvkuma@quicinc.com>

TrainingExtensions/torch/src/python/aimet_torch/meta/connectedgraph.py

@@ -746,12 +746,14 @@ def flatten_pack_producers(deconstruct_op: Op):
         if len(output_structure) == 1:
             self._output_structure = next(iter(output_structure.values()))
         else:
+            logger.warning("Unable to isolate model outputs.")
             self._output_structure = None
 
         # Remove inputs called "input_i" and populate their contents into a list based on their index
         self._input_structure = [input_structure.pop(f'input_{i}') for i in range(len(input_structure))]
         self._input_structure = self._input_structure[0] if len(self._input_structure) == 1 else self._input_structure
         if len(input_structure) != 0:
+            logger.warning("Unable to isolate model inputs.")
             self._input_structure = None
 
     @property

TrainingExtensions/torch/src/python/aimet_torch/utils.py

@@ -663,8 +663,10 @@ def replace_modules_with_instances_of_new_type(model: torch.nn.Module, modules_t
 def create_rand_tensors_given_shapes(input_shape: Union[Tuple, List[Tuple]], device: torch.device) \
         -> List[torch.Tensor]:
     """
-    Given shapes of some tensors, create one or more random tensors and return them as a list of tensors
-    :param input_shape: Shapes of tensors to create
+    Given shapes of some tensors, create one or more random tensors and return them as a list of tensors. Note that
+    tensor shapes must expressed as Tuples. A collection of tensor shapes may be represented as List or nested List of
+    tuples.
+    :param input_shape: Shapes of tensors to create (expressed as a Tuple, a List of Tuples, or a nested List of Tuples)
     :param device: Device to create tensors on
     :return: Created list of tensors
     """