diff --git a/python/ray/tests/test_tensorflow.py b/python/ray/tests/test_tensorflow.py
index d243deaae..ce4834992 100644
--- a/python/ray/tests/test_tensorflow.py
+++ b/python/ray/tests/test_tensorflow.py
@@ -4,7 +4,7 @@ import ray
 import ray.experimental.tf_utils
 from ray.rllib.utils.framework import try_import_tf
 
-tf = try_import_tf()
+tf, _, _ = try_import_tf()
 
 
 def make_linear_network(w_name=None, b_name=None):
diff --git a/rllib/agents/ddpg/tests/test_ddpg.py b/rllib/agents/ddpg/tests/test_ddpg.py
index 33580127f..de551cbd6 100644
--- a/rllib/agents/ddpg/tests/test_ddpg.py
+++ b/rllib/agents/ddpg/tests/test_ddpg.py
@@ -9,13 +9,12 @@ from ray.rllib.agents.ddpg.ddpg_torch_policy import ddpg_actor_critic_loss as \
 from ray.rllib.agents.sac.tests.test_sac import SimpleEnv
 from ray.rllib.execution.replay_buffer import LocalReplayBuffer
 from ray.rllib.policy.sample_batch import SampleBatch
-from ray.rllib.utils.framework import try_import_tf, try_import_torch
+from ray.rllib.utils.framework import try_import_torch
 from ray.rllib.utils.numpy import fc, huber_loss, l2_loss, relu, sigmoid
 from ray.rllib.utils.test_utils import check, check_compute_single_action, \
     framework_iterator
 from ray.rllib.utils.torch_ops import convert_to_torch_tensor
 
-tf = try_import_tf()
 torch, _ = try_import_torch()
 
 
diff --git a/rllib/agents/maml/tests/test_maml.py b/rllib/agents/maml/tests/test_maml.py
index 98b37ca78..a8daf260d 100644
--- a/rllib/agents/maml/tests/test_maml.py
+++ b/rllib/agents/maml/tests/test_maml.py
@@ -2,12 +2,9 @@ import unittest
 
 import ray
 import ray.rllib.agents.maml as maml
-from ray.rllib.utils.framework import try_import_tf
 from ray.rllib.utils.test_utils import check_compute_single_action, \
     framework_iterator
 
-tf = try_import_tf()
-
 
 class TestMAML(unittest.TestCase):
     @classmethod
diff --git a/rllib/policy/trajectory_view.py b/rllib/policy/trajectory_view.py
index e45858356..f1c8d4e22 100644
--- a/rllib/policy/trajectory_view.py
+++ b/rllib/policy/trajectory_view.py
@@ -1,11 +1,9 @@
-from dataclasses import dataclass
 import numpy as np
-from typing import Dict
+from typing import Dict, Optional
 
 from ray.rllib.utils.types import TensorType
 
 
-@dataclass
 class ViewRequirement:
     """Single view requirement (for one column in a ModelV2 input_dict).
 
@@ -24,29 +22,39 @@ class ViewRequirement:
         >>> print(req)
         {"obs": ViewRequirement(timesteps=0)}
     """
-    # The data column name from the SampleBatch (str key).
-    # If None, use the dict key under which this ViewRequirement resides.
-    data_col: str = None
 
-    # List of relative (or absolute timesteps) to be present in the
-    # input_dict.
-    timesteps: int = 0
+    def __init__(self,
+                 data_col: Optional[str] = None,
+                 timesteps: int = 0,
+                 fill_mode: str = "zeros",
+                 repeat_mode: str = "all"):
+        """Initializes a ViewRequirement object.
 
-    # Switch on absolute timestep mode. Default: False.
-    # TODO: (sven)
-    # "absolute_timesteps",
+        Args:
+            data_col (): The data column name from the SampleBatch (str key).
+                If None, use the dict key under which this ViewRequirement
+                resides.
+            timesteps (Union[List[int], int]): List of relative (or absolute
+                timesteps) to be present in the input_dict.
+            fill_mode (str): The fill mode in case t<0 or t>H.
+                One of "zeros", "tile".
+            repeat_mode (str): The repeat-mode (one of "all" or "only_first").
+                E.g. for training, we only want the first internal state
+                timestep (the NN will calculate all others again anyways).
+        """
+        self.data_col = data_col
+        self.timesteps = timesteps
 
-    # The fill mode in case t<0 or t>H: One of "zeros", "tile".
-    fill_mode: str = "zeros"
+        # Switch on absolute timestep mode. Default: False.
+        # TODO: (sven)
+        # "absolute_timesteps",
 
-    # The repeat-mode (one of "all" or "only_first"). E.g. for training,
-    # we only want the first internal state timestep (the NN will
-    # calculate all others again anyways).
-    repeat_mode: str = "all"
+        self.fill_mode = fill_mode
+        self.repeat_mode = repeat_mode
 
-    # Provide all data as time major (default: False).
-    # TODO: (sven)
-    # "time_major",
+        # Provide all data as time major (default: False).
+        # TODO: (sven)
+        # "time_major",
 
 
 def get_trajectory_view(
diff --git a/rllib/tuned_examples/debug_learning_failure_git_bisect.py b/rllib/tuned_examples/debug_learning_failure_git_bisect.py
index 84c0418a6..a5c89ab94 100644
--- a/rllib/tuned_examples/debug_learning_failure_git_bisect.py
+++ b/rllib/tuned_examples/debug_learning_failure_git_bisect.py
@@ -25,11 +25,8 @@ import yaml
 
 import ray
 from ray import tune
-from ray.rllib.utils.framework import try_import_tf
 from ray.rllib.utils.test_utils import check_learning_achieved
 
-tf = try_import_tf()
-
 parser = argparse.ArgumentParser()
 parser.add_argument("--run", type=str, default=None)
 parser.add_argument("--torch", action="store_true")