remove: shuffle=False option (#6886)

Fixes #6885 

### Description

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: Amirreza Aflakparast <[email protected]>
Signed-off-by: monai-bot <[email protected]>
Co-authored-by: monai-bot <[email protected]>

monai/data/samplers.py

@@ -81,7 +81,6 @@ class DistributedWeightedRandomSampler(DistributedSampler):
             by default, `world_size` is retrieved from the current distributed group.
         rank: rank of the current process within `num_replicas`. by default,
             `rank` is retrieved from the current distributed group.
-        shuffle: if `True`, sampler will shuffle the indices, default to True.
         kwargs: additional arguments for `DistributedSampler` super class, can be `seed` and `drop_last`.
 
     """
@@ -95,17 +94,10 @@ def __init__(
         even_divisible: bool = True,
         num_replicas: int | None = None,
         rank: int | None = None,
-        shuffle: bool = True,
         **kwargs,
     ):
-        super().__init__(
-            dataset=dataset,
-            even_divisible=even_divisible,
-            num_replicas=num_replicas,
-            rank=rank,
-            shuffle=shuffle,
-            **kwargs,
-        )
+        kwargs.setdefault("shuffle", True)
+        super().__init__(dataset=dataset, even_divisible=even_divisible, num_replicas=num_replicas, rank=rank, **kwargs)
         self.weights = weights
         self.num_samples_per_rank = num_samples_per_rank if num_samples_per_rank is not None else self.num_samples
         self.generator = generator

monai/losses/adversarial_loss.py

@@ -133,7 +133,7 @@ def forward(
                 "to False. To optimise a generator, target_is_real must be set to True."
             )
 
-        if type(input) is not list:
+        if not isinstance(input, list):
             input = [input]
         target_ = []
         for _, disc_out in enumerate(input):