fix remaining changes

Author: lhoestq

src/datasets/commands/datasets_cli.py

@@ -5,7 +5,7 @@
 from datasets.commands.dummy_data import DummyDataCommand
 from datasets.commands.env import EnvironmentCommand
 from datasets.commands.run_beam import RunBeamCommand
-from datasets.commands.test import CLITestCommand
+from datasets.commands.test import TestCommand
 from datasets.utils.logging import set_verbosity_info
 
 
@@ -23,7 +23,7 @@ def main():
     # Register commands
     ConvertCommand.register_subcommand(commands_parser)
     EnvironmentCommand.register_subcommand(commands_parser)
-    CLITestCommand.register_subcommand(commands_parser)
+    TestCommand.register_subcommand(commands_parser)
     RunBeamCommand.register_subcommand(commands_parser)
     DummyDataCommand.register_subcommand(commands_parser)
 

src/datasets/formatting/tf_formatter.py

@@ -65,9 +65,7 @@ def _tensorize(self, value):
     def _recursive_tensorize(self, data_struct: dict):
         # support for nested types like struct of list of struct
         if isinstance(data_struct, (list, np.ndarray)):
-            if data_struct.dtype == np.dtype(
-                "object"
-            ):  # tensorflow tensors can sometimes be instantied from an array of objects
+            if data_struct.dtype == object:  # tensorflow tensors can sometimes be instantied from an array of objects
                 try:
                     return self._tensorize(data_struct)
                 except ValueError:

src/datasets/formatting/torch_formatter.py

@@ -46,9 +46,7 @@ def _recursive_tensorize(self, data_struct: dict):
         # support for nested types like struct of list of struct
         if isinstance(data_struct, (list, np.ndarray)):
             data_struct = np.array(data_struct, copy=False)
-            if data_struct.dtype == np.dtype(
-                "object"
-            ):  # pytorch tensors cannot be instantied from an array of objects
+            if data_struct.dtype == object:  # pytorch tensors cannot be instantied from an array of objects
                 return [self.recursive_tensorize(substruct) for substruct in data_struct]
         return self._tensorize(data_struct)