diff --git a/tests/test_basics.py b/tests/test_basics.py
index e2501d9..f9039e2 100644
--- a/tests/test_basics.py
+++ b/tests/test_basics.py
@@ -1,38 +1,35 @@
-import openai
-from textgrad.variable import Variable
-from textgrad.optimizer import TextualGradientDescent
-from textgrad.engine.base import EngineLM
-from textgrad.engine.openai import ChatOpenAI
+import os
 import pytest
 import logging
-logging.disable(logging.CRITICAL)
 
 
-class DummyEngine(EngineLM):
+from textgrad import Variable, TextualGradientDescent, BlackboxLLM, sum
+from textgrad.engine.base import EngineLM
+from textgrad.engine.openai import ChatOpenAI
+from textgrad.autograd import LLMCall, FormattedLLMCall
 
+logging.disable(logging.CRITICAL)
+
+# Dummy engine that always returns "Hello World"
+class DummyEngine(EngineLM):
     def generate(self, prompt, system_prompt=None, **kwargs):
         return "Hello World"
 
     def __call__(self, prompt, system_prompt=None):
         return self.generate(prompt)
 
+# Idempotent engine that returns the prompt as is
 class IdempotentEngine(EngineLM):
-    
     def generate(self, prompt, system_prompt=None, **kwargs):
         return prompt
 
     def __call__(self, prompt, system_prompt=None):
         return self.generate(prompt)
 
-
+# Test if the variable object is passed correctly to the optimizer
 def test_variable_object_passing():
-    """
-    Test if the variable object is passed correctly to the optimizer
-    and if the edits propagate to the original variable
-    :return:
-    """
     v = Variable("Hello", role_description="A variable")
-    g = Variable("grad", role_description="A variable")
+    g = Variable("grad", role_description="A gradient")
     optimizer = TextualGradientDescent(parameters=[v], engine=DummyEngine())
 
     optimizer.parameters[0].gradients.add(g)
@@ -42,16 +39,15 @@ def test_variable_object_passing():
     assert optimizer.parameters[0].get_gradient_text() == "grad"
     assert v.value == "World"
 
+# Test the OpenAI engine initialization
 def test_openai_engine():
-
-    with pytest.raises(ValueError) as e:
+    with pytest.raises(ValueError):
         engine = ChatOpenAI()
 
-    import os
     os.environ['OPENAI_API_KEY'] = "fake_key"
     engine = ChatOpenAI()
 
-
+# Test importing main components from textgrad
 def test_import_main_components():
     from textgrad import Variable, TextualGradientDescent, EngineLM
     from textgrad.engine.openai import ChatOpenAI
@@ -63,22 +59,18 @@ def test_import_main_components():
     assert ChatOpenAI
     assert BlackboxLLM
 
+# Test a simple forward pass using the dummy engine
 def test_simple_forward_pass_engine():
-    from textgrad import BlackboxLLM
-    from textgrad import Variable
-
     text = Variable("Hello", role_description="A variable")
     dummy_engine = DummyEngine()
 
     engine = BlackboxLLM(engine=dummy_engine)
-
     response = engine(text)
 
     assert response
 
+# Test variable creation and attributes
 def test_variable_creation():
-    from textgrad import Variable
-
     text = Variable("Hello", role_description="A variable")
 
     assert text.value == "Hello"
@@ -86,41 +78,36 @@ def test_variable_creation():
 
     text = Variable("Hello", role_description="A variable", requires_grad=True)
 
-    assert text.requires_grad == True
+    assert text.requires_grad is True
     assert text.value == "Hello"
     assert text.role_description == "A variable"
 
-    with pytest.raises(TypeError) as e:
+    with pytest.raises(TypeError):
         text = Variable("Hello")
 
-
+# Test the sum function for variables
 def test_sum_function():
-    from textgrad import sum
-    from textgrad.variable import Variable
-
     var1 = Variable("Line1", role_description="role1")
     var2 = Variable("Line2", role_description="role2")
     total = sum(variables=[var1, var2])
+
     assert total.get_value() == "Line1\nLine2"
     assert "a combination of the following" in total.get_role_description()
     assert "role1" in total.get_role_description()
     assert "role2" in total.get_role_description()
-    
 
+# Test LLMCall using the dummy engine
 def test_llmcall():
-    from textgrad.autograd import LLMCall
-    from textgrad import Variable
     llm_call = LLMCall(DummyEngine())
     input_variable = Variable("Input", role_description="Input")
     output = llm_call(input_variable)
+
     assert isinstance(output, Variable)
     assert output.get_value() == "Hello World"
     assert input_variable in output.predecessors
-    
+
+# Test FormattedLLMCall using the idempotent engine
 def test_formattedllmcall():
-    from textgrad.autograd import FormattedLLMCall
-    from textgrad import Variable
-    
     format_string = "Question: {question}\nPrediction: {prediction}"
     fields = {"prediction": None, "question": None}
     formatted_llm_call = FormattedLLMCall(engine=IdempotentEngine(),
@@ -131,9 +118,9 @@ def test_formattedllmcall():
         "prediction": Variable("p", role_description="Prediction")
     }
     output = formatted_llm_call(inputs, response_role_description="test response")
-    
+
     assert isinstance(output, Variable)
     assert output.get_value() == "Question: q\nPrediction: p"
     assert inputs["question"] in output.predecessors
     assert inputs["prediction"] in output.predecessors
-    assert output.get_role_description() == "test response"
\ No newline at end of file
+    assert output.get_role_description() == "test response"