Apply isort and black reformatting

Signed-off-by: arendu <arendu@users.noreply.github.com>

nemo/collections/nlp/models/information_retrieval/megatron_gpt_embedding_model.py

@@ -61,7 +61,7 @@ def __init__(self, cfg: DictConfig, trainer: Trainer):
         if self.cfg.get("do_mrl", False):
             min_mrl = self.cfg.get("min_mrl_dim", int(np.log2(32))) - 1
             max_mrl = int(np.log2(self.cfg.hidden_size // 2))
-            self.mrl_dims = [2 ** i for i in range(max_mrl, min_mrl, -1)]
+            self.mrl_dims = [2**i for i in range(max_mrl, min_mrl, -1)]
         else:
             self.mrl_dims = []
 
@@ -262,7 +262,14 @@ def gather_and_maybe_write_predictions(self, output, data_cfg, mode, averaged_me
         gathered_output_batches = [None for _ in range(parallel_state.get_data_parallel_world_size())]
         torch.distributed.all_gather_object(
             gathered_output_batches,
-            [{'q_hs': batch['q_hs'], 'd_hs': batch['d_hs'], 'metadata': batch['metadata'],} for batch in output],
+            [
+                {
+                    'q_hs': batch['q_hs'],
+                    'd_hs': batch['d_hs'],
+                    'metadata': batch['metadata'],
+                }
+                for batch in output
+            ],
             group=parallel_state.get_data_parallel_group(),
         )
 
@@ -279,7 +286,11 @@ def gather_and_maybe_write_predictions(self, output, data_cfg, mode, averaged_me
                 l_d_hs = listify(batch['d_hs'])
                 l_m = batch['metadata']
                 assert len(l_m) == len(l_q_hs) == len(l_d_hs)
-                for q_hs, d_hs, metadata in zip(l_q_hs, l_d_hs, l_m,):
+                for q_hs, d_hs, metadata in zip(
+                    l_q_hs,
+                    l_d_hs,
+                    l_m,
+                ):
                     total_size += 1
                     if not metadata.get("__AUTOGENERATED__", False):
                         deduplicated_outputs['q_hs'].append(q_hs)
@@ -333,10 +344,10 @@ def write_embeddings_to_file(self, outputs, output_file_path, d_idx):
 
     def local_validation_step(self, dataloader_iter):
         """
-            Our dataloaders produce a micro-batch and then we fetch
-            a number of microbatches depending on the global batch size and model parallel size
-            from the dataloader to produce a list of microbatches.
-            The list of microbatches is then piped through the pipeline using megatron-core fwd/bwd functions.
+        Our dataloaders produce a micro-batch and then we fetch
+        a number of microbatches depending on the global batch size and model parallel size
+        from the dataloader to produce a list of microbatches.
+        The list of microbatches is then piped through the pipeline using megatron-core fwd/bwd functions.
         """
         # Check if iterator is exhausted
         # dataloader_iter, done = self._val_iterator_done(dataloader_iter)