remove slim package

config/ppo_transfer/CrawlerStatic.yaml

       learning_rate_schedule: linear
       encoder_layers: 2
       policy_layers: 1
+      feature_size: 64
     network_settings:
       normalize: true
       hidden_units: 512

ml-agents/mlagents/trainers/policy/transfer_policy.py

     GaussianDistribution,
     MultiCategoricalDistribution,
 )
-import tf_slim as slim
+# import tf_slim as slim
 EPSILON = 1e-6  # Small value to avoid divide by zero
 
 class GaussianEncoderDistribution:
         self.encoder_distribution = None
         self.targ_encoder = None
 
-        # Model-based learning
-        self.feature_size = 16  # dimension of latent feature size
         
         # Non-exposed parameters; these aren't exposed because they don't have a
         # good explanation and usually shouldn't be touched.
     def create_tf_graph(self, 
         encoder_layers = 1,
         policy_layers = 1,
+        feature_size = 16,
         transfer=False, 
         separate_train=False, 
         var_encoder=False,
         Builds the tensorflow graph needed for this policy.
         """
         self.inverse_model = inverse_model
-        self.reuse_encoder = transfer
+        self.reuse_encoder = reuse_encoder
+        self.feature_size = feature_size
 
         with self.graph.as_default():
             tf.set_random_seed(self.seed)

ml-agents/mlagents/trainers/ppo_transfer/optimizer.py

 from mlagents.trainers.optimizer.tf_optimizer import TFOptimizer
 from mlagents.trainers.buffer import AgentBuffer
 from mlagents.trainers.settings import TrainerSettings, PPOSettings, PPOTransferSettings
-import tf_slim as slim
+# import tf_slim as slim
 
 class PPOTransferOptimizer(TFOptimizer):
     def __init__(self, policy: TransferPolicy, trainer_params: TrainerSettings):
         self.model_update_dict: Dict[str, tf.Tensor] = {}
 
         # Create the graph here to give more granular control of the TF graph to the Optimizer.
-        policy.create_tf_graph(hyperparameters.encoder_layers, hyperparameters.policy_layers, 
+        policy.create_tf_graph(hyperparameters.encoder_layers, hyperparameters.policy_layers, hyperparameters.feature_size,
             self.use_transfer, self.separate_policy_train, self.use_var_encoder, self.use_var_predict, 
             self.predict_return, self.use_inverse_model, self.reuse_encoder)
         
             # saver.restore(self.sess, model_checkpoint)
             # self.policy._set_step(0)
 
-            slim.model_analyzer.analyze_vars(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES), print_info=True)
+            # slim.model_analyzer.analyze_vars(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES), print_info=True)
             
             print("All variables in the graph:")
             for variable in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):

ml-agents/mlagents/trainers/settings.py

 
 def check_and_structure(key: str, value: Any, class_type: type) -> Any:
     attr_fields_dict = attr.fields_dict(class_type)
-    print(attr_fields_dict)
     if key not in attr_fields_dict:
         raise TrainerConfigError(
             f"The option {key} was specified in your YAML file for {class_type.__name__}, but is invalid."
     in_epoch_alter: bool = False
     use_op_buffer: bool = False
     train_type: str = "all"
+    feature_size: int = 16
         
     # Transfer
     use_transfer: bool = False