change: enable signature-differs pylint check (#915)

Author: knakad

.pylintrc

@@ -87,7 +87,6 @@ disable=
     attribute-defined-outside-init, # TODO: Fix scope
     protected-access, # TODO: Fix access
     abstract-method, # TODO: Fix abstract methods
-    signature-differs, # TODO: Fix signature overrides
     unidiomatic-typecheck, # TODO: Fix typechecks
     wrong-import-order, # TODO: Fix import order
     no-else-return, # TODO: Remove unnecessary elses

src/sagemaker/amazon/lda.py

@@ -120,7 +120,9 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
             vpc_config=self.get_vpc_config(vpc_config_override),
         )
 
-    def _prepare_for_training(self, records, mini_batch_size, job_name=None):
+    def _prepare_for_training(  # pylint: disable=signature-differs
+        self, records, mini_batch_size, job_name=None
+    ):
         # mini_batch_size is required, prevent explicit calls with None
         if mini_batch_size is None:
             raise ValueError("mini_batch_size must be set")

src/sagemaker/amazon/ntm.py

@@ -151,7 +151,9 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT):
             vpc_config=self.get_vpc_config(vpc_config_override),
         )
 
-    def _prepare_for_training(self, records, mini_batch_size, job_name=None):
+    def _prepare_for_training(  # pylint: disable=signature-differs
+        self, records, mini_batch_size, job_name=None
+    ):
         if mini_batch_size is not None and (mini_batch_size < 1 or mini_batch_size > 10000):
             raise ValueError("mini_batch_size must be in [1, 10000]")
         super(NTM, self)._prepare_for_training(