Remove workflow code from dataloaders

We should be doing online transforms like
```KerasSequenceLoader(workflow.transform(dataset), ...```  instead of
```KerasSequenceLoader(dataset, workflows=[workflow], ...``` now

nvtabular/loader/backend.py

@@ -23,7 +23,6 @@
 
 from nvtabular.io.shuffle import _shuffle_gdf
 from nvtabular.ops import _get_embedding_order
-from nvtabular.workflow import BaseWorkflow
 
 
 def _num_steps(num_samples, step_size):
@@ -155,21 +154,6 @@ def get_batch_div_chunk(self, chunks, batch_size):
         return chunks, spill
 
 
-def _validate_workflows(workflows, cat_names, cont_names, label_names):
-    assert all([isinstance(w, BaseWorkflow) for w in workflows])
-    # TODO: commenting out until it's clearer what the
-    # columns in workflow.columns_cts["final"]["ctx"] mean
-    # for workflow in workflows:
-    #     assert set(workflow.columns_ctx["categorical"]["base"]) == set(cat_names)
-    #     assert set(workflow.columns_ctx["continuous"]["base"]) == set(cont_names)
-    #     assert set(workflow.columns_ctx["label"]["base"]) == set(label_names)
-
-    #     cat_names = workflow.columns_ctx["final"]["ctx"]["categorical"]
-    #     cont_names = workflow.columns_ctx["final"]["ctx"]["continuous"]
-    #     label_name = workflow.columns_ctx["final"]["ctx"]["label"][0]
-    return workflows
-
-
 # TODO: implement as metaclass and assign methods to children
 # to avoid having to do Dataset.<method> calls?
 class DataLoader:
@@ -184,15 +168,12 @@ def __init__(
         batch_size,
         shuffle,
         parts_per_chunk=1,
-        workflows=None,
         devices=None,
     ):
         self.data = dataset
         self.indices = cp.arange(dataset.to_ddf().npartitions)
 
         devices = devices or [0]
-        workflows = workflows or []
-        self.workflows = _validate_workflows(workflows, cat_names, cont_names, label_names)
 
         self.cat_names = cat_names or []
         self.cont_names = cont_names or []
@@ -298,20 +279,7 @@ def _get_next_batch(self):
             batch = next(self._batch_itr)
         return batch
 
-    def map(self, workflow):
-        """
-        Map an NVTabular Workflow on to a data loader to do
-        online preprocessing
-        """
-        workflows = self.workflows + [workflow]
-        self.workflows = _validate_workflows(
-            workflows, self.cat_names, self.cont_names, self.label_names
-        )
-        # TODO: update cat/cont/label names after
-
     def make_tensors(self, gdf, use_nnz=False):
-        for workflow in self.workflows:
-            gdf = workflow.apply_ops(gdf)
         split_idx = self._get_segment_lengths(len(gdf))
 
         # map from big chunk to framework-specific tensors