diff --git a/.github/workflows/minitorch.yml b/.github/workflows/minitorch.yml
index a03fe01..0843fd8 100644
--- a/.github/workflows/minitorch.yml
+++ b/.github/workflows/minitorch.yml
@@ -26,30 +26,43 @@ jobs:
       run: |
         # stop the build if there are Python syntax errors or undefined names
         flake8 --ignore "N801, E203, E266, E501, W503, F812, F401, F841, E741, N803, N802, N806" minitorch/ tests/ project/
-    - name: Test with pytest
+
+    - name: Test Module 0
       run: |
         echo "Module 0"
         pytest tests -x -m task0_1
         pytest tests -x -m task0_2
         pytest tests -x -m task0_3
         pytest tests -x -m task0_4
+
+    - name: Test Module 1
+      run: |
         echo "Module 1"
         pytest tests -x -m task1_1
         pytest tests -x -m task1_2
         pytest tests -x -m task1_3
         pytest tests -x -m task1_4
+
+    - name: Test Module 2
+      run: |
         echo "Module 2"
         pytest tests -x -m task2_1
         pytest tests -x -m task2_2
         pytest tests -x -m task2_3
         pytest tests -x -m task2_4
+
+    - name: Test Module 3
+      run: |
         echo "Module 3"
         pytest tests -x -m task3_1
         pytest tests -x -m task3_2
         pytest tests -x -m task3_3
         pytest tests -x -m task3_4
+
+    - name: Test Module 4
+      run: |
         echo "Module 4"
         pytest tests -x -m task4_1
         pytest tests -x -m task4_2
         pytest tests -x -m task4_3
-        pytest tests -x -m task4_4
+        pytest tests -x -m task4_4
\ No newline at end of file
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 0000000..4e84492
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,19 @@
+name: minitorch-conda-env-py38
+
+channels:
+  - conda-forge
+
+dependencies:
+  - python=3.8
+  - chalk-diagram
+  - colorama==0.4.3
+  - hypothesis==6.54
+  - mypy==0.971
+  - numba==0.56
+  - numpy==1.22
+  - pre-commit==2.20.0
+  - pytest==7.1.2
+  - pytest-env
+  - pytest-runner==5.2
+  - typing_extensions
+  - colour==0.1.5
diff --git a/minitorch/autodiff.py b/minitorch/autodiff.py
index 06496e2..9431908 100644
--- a/minitorch/autodiff.py
+++ b/minitorch/autodiff.py
@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import Any, Iterable, List, Tuple
+from typing import Any, Iterable, List, Tuple  # noqa: F401
 
 from typing_extensions import Protocol
 
@@ -23,7 +23,7 @@ def central_difference(f: Any, *vals: Any, arg: int = 0, epsilon: float = 1e-6)
         An approximation of $f'_i(x_0, \ldots, x_{n-1})$
     """
     # TODO: Implement for Task 1.1.
-    raise NotImplementedError('Need to implement for Task 1.1')
+    raise NotImplementedError("Need to implement for Task 1.1")
 
 
 variable_count = 1
@@ -62,7 +62,7 @@ def topological_sort(variable: Variable) -> Iterable[Variable]:
         Non-constant Variables in topological order starting from the right.
     """
     # TODO: Implement for Task 1.4.
-    raise NotImplementedError('Need to implement for Task 1.4')
+    raise NotImplementedError("Need to implement for Task 1.4")
 
 
 def backpropagate(variable: Variable, deriv: Any) -> None:
@@ -77,7 +77,7 @@ def backpropagate(variable: Variable, deriv: Any) -> None:
     No return. Should write to its results to the derivative values of each leaf through `accumulate_derivative`.
     """
     # TODO: Implement for Task 1.4.
-    raise NotImplementedError('Need to implement for Task 1.4')
+    raise NotImplementedError("Need to implement for Task 1.4")
 
 
 @dataclass
diff --git a/minitorch/cuda_ops.py b/minitorch/cuda_ops.py
index ac4cbae..f5fc13f 100644
--- a/minitorch/cuda_ops.py
+++ b/minitorch/cuda_ops.py
@@ -150,11 +150,11 @@ def _map(
         in_strides: Strides,
     ) -> None:
 
-        out_index = cuda.local.array(MAX_DIMS, numba.int32)
-        in_index = cuda.local.array(MAX_DIMS, numba.int32)
-        i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x
+        out_index = cuda.local.array(MAX_DIMS, numba.int32)  # noqa: F841
+        in_index = cuda.local.array(MAX_DIMS, numba.int32)  # noqa: F841
+        i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x  # noqa: F841
         # TODO: Implement for Task 3.3.
-        raise NotImplementedError('Need to implement for Task 3.3')
+        raise NotImplementedError("Need to implement for Task 3.3")
 
     return cuda.jit()(_map)  # type: ignore
 
@@ -190,13 +190,13 @@ def _zip(
         b_strides: Strides,
     ) -> None:
 
-        out_index = cuda.local.array(MAX_DIMS, numba.int32)
-        a_index = cuda.local.array(MAX_DIMS, numba.int32)
-        b_index = cuda.local.array(MAX_DIMS, numba.int32)
-        i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x
+        out_index = cuda.local.array(MAX_DIMS, numba.int32)  # noqa: F841
+        a_index = cuda.local.array(MAX_DIMS, numba.int32)  # noqa: F841
+        b_index = cuda.local.array(MAX_DIMS, numba.int32)  # noqa: F841
+        i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x  # noqa: F841
 
         # TODO: Implement for Task 3.3.
-        raise NotImplementedError('Need to implement for Task 3.3')
+        raise NotImplementedError("Need to implement for Task 3.3")
 
     return cuda.jit()(_zip)  # type: ignore
 
@@ -224,12 +224,12 @@ def _sum_practice(out: Storage, a: Storage, size: int) -> None:
     """
     BLOCK_DIM = 32
 
-    cache = cuda.shared.array(BLOCK_DIM, numba.float64)
-    i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x
-    pos = cuda.threadIdx.x
+    cache = cuda.shared.array(BLOCK_DIM, numba.float64)  # noqa: F841
+    i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x  # noqa: F841
+    pos = cuda.threadIdx.x  # noqa: F841
 
     # TODO: Implement for Task 3.3.
-    raise NotImplementedError('Need to implement for Task 3.3')
+    raise NotImplementedError("Need to implement for Task 3.3")
 
 
 jit_sum_practice = cuda.jit()(_sum_practice)
@@ -273,13 +273,13 @@ def _reduce(
         reduce_value: float,
     ) -> None:
         BLOCK_DIM = 1024
-        cache = cuda.shared.array(BLOCK_DIM, numba.float64)
-        out_index = cuda.local.array(MAX_DIMS, numba.int32)
-        out_pos = cuda.blockIdx.x
-        pos = cuda.threadIdx.x
+        cache = cuda.shared.array(BLOCK_DIM, numba.float64)  # noqa: F841
+        out_index = cuda.local.array(MAX_DIMS, numba.int32)  # noqa: F841
+        out_pos = cuda.blockIdx.x  # noqa: F841
+        pos = cuda.threadIdx.x  # noqa: F841
 
         # TODO: Implement for Task 3.3.
-        raise NotImplementedError('Need to implement for Task 3.3')
+        raise NotImplementedError("Need to implement for Task 3.3")
 
     return cuda.jit()(_reduce)  # type: ignore
 
@@ -314,9 +314,9 @@ def _mm_practice(out: Storage, a: Storage, b: Storage, size: int) -> None:
         b (Storage): storage for `b` tensor.
         size (int): size of the square
     """
-    BLOCK_DIM = 32
+    BLOCK_DIM = 32  # noqa: F841
     # TODO: Implement for Task 3.3.
-    raise NotImplementedError('Need to implement for Task 3.3')
+    raise NotImplementedError("Need to implement for Task 3.3")
 
 
 jit_mm_practice = cuda.jit()(_mm_practice)
@@ -363,22 +363,22 @@ def _tensor_matrix_multiply(
     Returns:
         None : Fills in `out`
     """
-    a_batch_stride = a_strides[0] if a_shape[0] > 1 else 0
-    b_batch_stride = b_strides[0] if b_shape[0] > 1 else 0
+    a_batch_stride = a_strides[0] if a_shape[0] > 1 else 0  # noqa: F841
+    b_batch_stride = b_strides[0] if b_shape[0] > 1 else 0  # noqa: F841
     # Batch dimension - fixed
-    batch = cuda.blockIdx.z
+    batch = cuda.blockIdx.z  # noqa: F841
 
     BLOCK_DIM = 32
-    a_shared = cuda.shared.array((BLOCK_DIM, BLOCK_DIM), numba.float64)
-    b_shared = cuda.shared.array((BLOCK_DIM, BLOCK_DIM), numba.float64)
+    a_shared = cuda.shared.array((BLOCK_DIM, BLOCK_DIM), numba.float64)  # noqa: F841
+    b_shared = cuda.shared.array((BLOCK_DIM, BLOCK_DIM), numba.float64)  # noqa: F841
 
     # The final position c[i, j]
-    i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x
-    j = cuda.blockIdx.y * cuda.blockDim.y + cuda.threadIdx.y
+    i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x  # noqa: F841
+    j = cuda.blockIdx.y * cuda.blockDim.y + cuda.threadIdx.y  # noqa: F841
 
     # The local position in the block.
-    pi = cuda.threadIdx.x
-    pj = cuda.threadIdx.y
+    pi = cuda.threadIdx.x  # noqa: F841
+    pj = cuda.threadIdx.y  # noqa: F841
 
     # Code Plan:
     # 1) Move across shared dimension by block dim.
@@ -386,7 +386,7 @@ def _tensor_matrix_multiply(
     #    b) Copy into shared memory for b matrix
     #    c) Compute the dot produce for position c[i, j]
     # TODO: Implement for Task 3.4.
-    raise NotImplementedError('Need to implement for Task 3.4')
+    raise NotImplementedError("Need to implement for Task 3.4")
 
 
 tensor_matrix_multiply = cuda.jit(_tensor_matrix_multiply)
diff --git a/minitorch/fast_conv.py b/minitorch/fast_conv.py
index ce4244c..f9dff39 100644
--- a/minitorch/fast_conv.py
+++ b/minitorch/fast_conv.py
@@ -1,13 +1,12 @@
 from typing import Tuple
 
-import numpy as np
-from numba import njit, prange
+import numpy as np  # noqa: F401
+from numba import njit, prange  # noqa: F401
 
 from .autodiff import Context
 from .tensor import Tensor
+from .tensor_data import MAX_DIMS, Index  # noqa: F401
 from .tensor_data import (
-    MAX_DIMS,
-    Index,
     Shape,
     Strides,
     broadcast_index,
@@ -77,11 +76,11 @@ def _tensor_conv1d(
         and in_channels == in_channels_
         and out_channels == out_channels_
     )
-    s1 = input_strides
-    s2 = weight_strides
+    s1 = input_strides  # noqa: F841
+    s2 = weight_strides  # noqa: F841
 
     # TODO: Implement for Task 4.1.
-    raise NotImplementedError('Need to implement for Task 4.1')
+    raise NotImplementedError("Need to implement for Task 4.1")
 
 
 tensor_conv1d = njit(parallel=True)(_tensor_conv1d)
@@ -203,11 +202,11 @@ def _tensor_conv2d(
     s1 = input_strides
     s2 = weight_strides
     # inners
-    s10, s11, s12, s13 = s1[0], s1[1], s1[2], s1[3]
-    s20, s21, s22, s23 = s2[0], s2[1], s2[2], s2[3]
+    s10, s11, s12, s13 = s1[0], s1[1], s1[2], s1[3]  # noqa: F841
+    s20, s21, s22, s23 = s2[0], s2[1], s2[2], s2[3]  # noqa: F841
 
     # TODO: Implement for Task 4.2.
-    raise NotImplementedError('Need to implement for Task 4.2')
+    raise NotImplementedError("Need to implement for Task 4.2")
 
 
 tensor_conv2d = njit(parallel=True, fastmath=True)(_tensor_conv2d)
diff --git a/minitorch/fast_ops.py b/minitorch/fast_ops.py
index dc73b86..4b19b09 100644
--- a/minitorch/fast_ops.py
+++ b/minitorch/fast_ops.py
@@ -2,11 +2,12 @@
 
 from typing import TYPE_CHECKING
 
-import numpy as np
-from numba import njit, prange
+import numpy as np  # noqa: F401
+from numba import njit
+from numba import prange  # noqa: F401
 
+from .tensor_data import MAX_DIMS  # noqa: F401
 from .tensor_data import (
-    MAX_DIMS,
     broadcast_index,
     index_to_position,
     shape_broadcast,
@@ -18,7 +19,8 @@
     from typing import Callable, Optional
 
     from .tensor import Tensor
-    from .tensor_data import Index, Shape, Storage, Strides
+    from .tensor_data import Index  # noqa: F401
+    from .tensor_data import Shape, Storage, Strides
 
 # TIP: Use `NUMBA_DISABLE_JIT=1 pytest tests/ -m task3_1` to run these tests without JIT.
 
@@ -160,7 +162,7 @@ def _map(
         in_strides: Strides,
     ) -> None:
         # TODO: Implement for Task 3.1.
-        raise NotImplementedError('Need to implement for Task 3.1')
+        raise NotImplementedError("Need to implement for Task 3.1")
 
     return njit(parallel=True)(_map)  # type: ignore
 
@@ -199,7 +201,7 @@ def _zip(
         b_strides: Strides,
     ) -> None:
         # TODO: Implement for Task 3.1.
-        raise NotImplementedError('Need to implement for Task 3.1')
+        raise NotImplementedError("Need to implement for Task 3.1")
 
     return njit(parallel=True)(_zip)  # type: ignore
 
@@ -233,7 +235,7 @@ def _reduce(
         reduce_dim: int,
     ) -> None:
         # TODO: Implement for Task 3.1.
-        raise NotImplementedError('Need to implement for Task 3.1')
+        raise NotImplementedError("Need to implement for Task 3.1")
 
     return njit(parallel=True)(_reduce)  # type: ignore
 
@@ -279,11 +281,11 @@ def _tensor_matrix_multiply(
     Returns:
         None : Fills in `out`
     """
-    a_batch_stride = a_strides[0] if a_shape[0] > 1 else 0
-    b_batch_stride = b_strides[0] if b_shape[0] > 1 else 0
+    a_batch_stride = a_strides[0] if a_shape[0] > 1 else 0  # noqa: F841
+    b_batch_stride = b_strides[0] if b_shape[0] > 1 else 0  # noqa: F841
 
     # TODO: Implement for Task 3.2.
-    raise NotImplementedError('Need to implement for Task 3.2')
+    raise NotImplementedError("Need to implement for Task 3.2")
 
 
 tensor_matrix_multiply = njit(parallel=True, fastmath=True)(_tensor_matrix_multiply)
diff --git a/minitorch/module.py b/minitorch/module.py
index d32c609..8f17cfb 100644
--- a/minitorch/module.py
+++ b/minitorch/module.py
@@ -32,12 +32,12 @@ def modules(self) -> Sequence[Module]:
     def train(self) -> None:
         "Set the mode of this module and all descendent modules to `train`."
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError('Need to implement for Task 0.4')
+        raise NotImplementedError("Need to implement for Task 0.4")
 
     def eval(self) -> None:
         "Set the mode of this module and all descendent modules to `eval`."
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError('Need to implement for Task 0.4')
+        raise NotImplementedError("Need to implement for Task 0.4")
 
     def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
         """
@@ -48,12 +48,12 @@ def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
             The name and `Parameter` of each ancestor parameter.
         """
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError('Need to implement for Task 0.4')
+        raise NotImplementedError("Need to implement for Task 0.4")
 
     def parameters(self) -> Sequence[Parameter]:
         "Enumerate over all the parameters of this module and its descendents."
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError('Need to implement for Task 0.4')
+        raise NotImplementedError("Need to implement for Task 0.4")
 
     def add_parameter(self, k: str, v: Any) -> Parameter:
         """
diff --git a/minitorch/nn.py b/minitorch/nn.py
index 92c0c8f..e39e4fb 100644
--- a/minitorch/nn.py
+++ b/minitorch/nn.py
@@ -4,7 +4,7 @@
 from .autodiff import Context
 from .fast_ops import FastOps
 from .tensor import Tensor
-from .tensor_functions import Function, rand, tensor
+from .tensor_functions import Function, rand, tensor  # noqa: F401
 
 
 def tile(input: Tensor, kernel: Tuple[int, int]) -> Tuple[Tensor, int, int]:
@@ -24,7 +24,7 @@ def tile(input: Tensor, kernel: Tuple[int, int]) -> Tuple[Tensor, int, int]:
     assert height % kh == 0
     assert width % kw == 0
     # TODO: Implement for Task 4.3.
-    raise NotImplementedError('Need to implement for Task 4.3')
+    raise NotImplementedError("Need to implement for Task 4.3")
 
 
 def avgpool2d(input: Tensor, kernel: Tuple[int, int]) -> Tensor:
@@ -40,7 +40,7 @@ def avgpool2d(input: Tensor, kernel: Tuple[int, int]) -> Tensor:
     """
     batch, channel, height, width = input.shape
     # TODO: Implement for Task 4.3.
-    raise NotImplementedError('Need to implement for Task 4.3')
+    raise NotImplementedError("Need to implement for Task 4.3")
 
 
 max_reduce = FastOps.reduce(operators.max, -1e9)
@@ -68,13 +68,13 @@ class Max(Function):
     def forward(ctx: Context, input: Tensor, dim: Tensor) -> Tensor:
         "Forward of max should be max reduction"
         # TODO: Implement for Task 4.4.
-        raise NotImplementedError('Need to implement for Task 4.4')
+        raise NotImplementedError("Need to implement for Task 4.4")
 
     @staticmethod
     def backward(ctx: Context, grad_output: Tensor) -> Tuple[Tensor, float]:
         "Backward of max should be argmax (see above)"
         # TODO: Implement for Task 4.4.
-        raise NotImplementedError('Need to implement for Task 4.4')
+        raise NotImplementedError("Need to implement for Task 4.4")
 
 
 def max(input: Tensor, dim: int) -> Tensor:
@@ -97,7 +97,7 @@ def softmax(input: Tensor, dim: int) -> Tensor:
         softmax tensor
     """
     # TODO: Implement for Task 4.4.
-    raise NotImplementedError('Need to implement for Task 4.4')
+    raise NotImplementedError("Need to implement for Task 4.4")
 
 
 def logsoftmax(input: Tensor, dim: int) -> Tensor:
@@ -116,7 +116,7 @@ def logsoftmax(input: Tensor, dim: int) -> Tensor:
          log of softmax tensor
     """
     # TODO: Implement for Task 4.4.
-    raise NotImplementedError('Need to implement for Task 4.4')
+    raise NotImplementedError("Need to implement for Task 4.4")
 
 
 def maxpool2d(input: Tensor, kernel: Tuple[int, int]) -> Tensor:
@@ -132,7 +132,7 @@ def maxpool2d(input: Tensor, kernel: Tuple[int, int]) -> Tensor:
     """
     batch, channel, height, width = input.shape
     # TODO: Implement for Task 4.4.
-    raise NotImplementedError('Need to implement for Task 4.4')
+    raise NotImplementedError("Need to implement for Task 4.4")
 
 
 def dropout(input: Tensor, rate: float, ignore: bool = False) -> Tensor:
@@ -148,4 +148,4 @@ def dropout(input: Tensor, rate: float, ignore: bool = False) -> Tensor:
         tensor with random positions dropped out
     """
     # TODO: Implement for Task 4.4.
-    raise NotImplementedError('Need to implement for Task 4.4')
+    raise NotImplementedError("Need to implement for Task 4.4")
diff --git a/minitorch/scalar.py b/minitorch/scalar.py
index 942079d..aa36ddb 100644
--- a/minitorch/scalar.py
+++ b/minitorch/scalar.py
@@ -6,18 +6,18 @@
 import numpy as np
 
 from .autodiff import Context, Variable, backpropagate, central_difference
+from .scalar_functions import EQ  # noqa: F401
+from .scalar_functions import LT  # noqa: F401
+from .scalar_functions import Add  # noqa: F401
+from .scalar_functions import Exp  # noqa: F401
+from .scalar_functions import Log  # noqa: F401
+from .scalar_functions import Neg  # noqa: F401
+from .scalar_functions import ReLU  # noqa: F401
+from .scalar_functions import Sigmoid  # noqa: F401
 from .scalar_functions import (
-    EQ,
-    LT,
-    Add,
-    Exp,
     Inv,
-    Log,
     Mul,
-    Neg,
-    ReLU,
     ScalarFunction,
-    Sigmoid,
 )
 
 ScalarLike = Union[float, int, "Scalar"]
@@ -93,30 +93,30 @@ def __rtruediv__(self, b: ScalarLike) -> Scalar:
 
     def __add__(self, b: ScalarLike) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __bool__(self) -> bool:
         return bool(self.data)
 
     def __lt__(self, b: ScalarLike) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __gt__(self, b: ScalarLike) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __eq__(self, b: ScalarLike) -> Scalar:  # type: ignore[override]
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __sub__(self, b: ScalarLike) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __neg__(self) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def __radd__(self, b: ScalarLike) -> Scalar:
         return self + b
@@ -126,19 +126,19 @@ def __rmul__(self, b: ScalarLike) -> Scalar:
 
     def log(self) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def exp(self) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def sigmoid(self) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     def relu(self) -> Scalar:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     # Variable elements for backprop
 
@@ -174,7 +174,7 @@ def chain_rule(self, d_output: Any) -> Iterable[Tuple[Variable, Any]]:
         assert h.ctx is not None
 
         # TODO: Implement for Task 1.3.
-        raise NotImplementedError('Need to implement for Task 1.3')
+        raise NotImplementedError("Need to implement for Task 1.3")
 
     def backward(self, d_output: Optional[float] = None) -> None:
         """
diff --git a/minitorch/scalar_functions.py b/minitorch/scalar_functions.py
index b5deab0..d8d2307 100644
--- a/minitorch/scalar_functions.py
+++ b/minitorch/scalar_functions.py
@@ -104,12 +104,12 @@ class Mul(ScalarFunction):
     @staticmethod
     def forward(ctx: Context, a: float, b: float) -> float:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     @staticmethod
     def backward(ctx: Context, d_output: float) -> Tuple[float, float]:
         # TODO: Implement for Task 1.4.
-        raise NotImplementedError('Need to implement for Task 1.4')
+        raise NotImplementedError("Need to implement for Task 1.4")
 
 
 class Inv(ScalarFunction):
@@ -118,12 +118,12 @@ class Inv(ScalarFunction):
     @staticmethod
     def forward(ctx: Context, a: float) -> float:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     @staticmethod
     def backward(ctx: Context, d_output: float) -> float:
         # TODO: Implement for Task 1.4.
-        raise NotImplementedError('Need to implement for Task 1.4')
+        raise NotImplementedError("Need to implement for Task 1.4")
 
 
 class Neg(ScalarFunction):
@@ -132,12 +132,12 @@ class Neg(ScalarFunction):
     @staticmethod
     def forward(ctx: Context, a: float) -> float:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     @staticmethod
     def backward(ctx: Context, d_output: float) -> float:
         # TODO: Implement for Task 1.4.
-        raise NotImplementedError('Need to implement for Task 1.4')
+        raise NotImplementedError("Need to implement for Task 1.4")
 
 
 class Sigmoid(ScalarFunction):
@@ -146,12 +146,12 @@ class Sigmoid(ScalarFunction):
     @staticmethod
     def forward(ctx: Context, a: float) -> float:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     @staticmethod
     def backward(ctx: Context, d_output: float) -> float:
         # TODO: Implement for Task 1.4.
-        raise NotImplementedError('Need to implement for Task 1.4')
+        raise NotImplementedError("Need to implement for Task 1.4")
 
 
 class ReLU(ScalarFunction):
@@ -160,12 +160,12 @@ class ReLU(ScalarFunction):
     @staticmethod
     def forward(ctx: Context, a: float) -> float:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     @staticmethod
     def backward(ctx: Context, d_output: float) -> float:
         # TODO: Implement for Task 1.4.
-        raise NotImplementedError('Need to implement for Task 1.4')
+        raise NotImplementedError("Need to implement for Task 1.4")
 
 
 class Exp(ScalarFunction):
@@ -174,12 +174,12 @@ class Exp(ScalarFunction):
     @staticmethod
     def forward(ctx: Context, a: float) -> float:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     @staticmethod
     def backward(ctx: Context, d_output: float) -> float:
         # TODO: Implement for Task 1.4.
-        raise NotImplementedError('Need to implement for Task 1.4')
+        raise NotImplementedError("Need to implement for Task 1.4")
 
 
 class LT(ScalarFunction):
@@ -188,12 +188,12 @@ class LT(ScalarFunction):
     @staticmethod
     def forward(ctx: Context, a: float, b: float) -> float:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     @staticmethod
     def backward(ctx: Context, d_output: float) -> Tuple[float, float]:
         # TODO: Implement for Task 1.4.
-        raise NotImplementedError('Need to implement for Task 1.4')
+        raise NotImplementedError("Need to implement for Task 1.4")
 
 
 class EQ(ScalarFunction):
@@ -202,9 +202,9 @@ class EQ(ScalarFunction):
     @staticmethod
     def forward(ctx: Context, a: float, b: float) -> float:
         # TODO: Implement for Task 1.2.
-        raise NotImplementedError('Need to implement for Task 1.2')
+        raise NotImplementedError("Need to implement for Task 1.2")
 
     @staticmethod
     def backward(ctx: Context, d_output: float) -> Tuple[float, float]:
         # TODO: Implement for Task 1.4.
-        raise NotImplementedError('Need to implement for Task 1.4')
+        raise NotImplementedError("Need to implement for Task 1.4")
diff --git a/minitorch/tensor_data.py b/minitorch/tensor_data.py
index 1d4a0c9..7dc0fc1 100644
--- a/minitorch/tensor_data.py
+++ b/minitorch/tensor_data.py
@@ -44,7 +44,7 @@ def index_to_position(index: Index, strides: Strides) -> int:
     """
 
     # TODO: Implement for Task 2.1.
-    raise NotImplementedError('Need to implement for Task 2.1')
+    raise NotImplementedError("Need to implement for Task 2.1")
 
 
 def to_index(ordinal: int, shape: Shape, out_index: OutIndex) -> None:
@@ -61,7 +61,7 @@ def to_index(ordinal: int, shape: Shape, out_index: OutIndex) -> None:
 
     """
     # TODO: Implement for Task 2.1.
-    raise NotImplementedError('Need to implement for Task 2.1')
+    raise NotImplementedError("Need to implement for Task 2.1")
 
 
 def broadcast_index(
@@ -84,7 +84,7 @@ def broadcast_index(
         None
     """
     # TODO: Implement for Task 2.2.
-    raise NotImplementedError('Need to implement for Task 2.2')
+    raise NotImplementedError("Need to implement for Task 2.2")
 
 
 def shape_broadcast(shape1: UserShape, shape2: UserShape) -> UserShape:
@@ -102,7 +102,7 @@ def shape_broadcast(shape1: UserShape, shape2: UserShape) -> UserShape:
         IndexingError : if cannot broadcast
     """
     # TODO: Implement for Task 2.2.
-    raise NotImplementedError('Need to implement for Task 2.2')
+    raise NotImplementedError("Need to implement for Task 2.2")
 
 
 def strides_from_shape(shape: UserShape) -> UserStrides:
@@ -228,7 +228,7 @@ def permute(self, *order: int) -> TensorData:
         ), f"Must give a position to each dimension. Shape: {self.shape} Order: {order}"
 
         # TODO: Implement for Task 2.1.
-        raise NotImplementedError('Need to implement for Task 2.1')
+        raise NotImplementedError("Need to implement for Task 2.1")
 
     def to_string(self) -> str:
         s = ""
diff --git a/minitorch/tensor_functions.py b/minitorch/tensor_functions.py
index f1c0547..86db01a 100644
--- a/minitorch/tensor_functions.py
+++ b/minitorch/tensor_functions.py
@@ -100,60 +100,60 @@ class Mul(Function):
     @staticmethod
     def forward(ctx: Context, a: Tensor, b: Tensor) -> Tensor:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
     @staticmethod
     def backward(ctx: Context, grad_output: Tensor) -> Tuple[Tensor, Tensor]:
         # TODO: Implement for Task 2.4.
-        raise NotImplementedError('Need to implement for Task 2.4')
+        raise NotImplementedError("Need to implement for Task 2.4")
 
 
 class Sigmoid(Function):
     @staticmethod
     def forward(ctx: Context, t1: Tensor) -> Tensor:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
     @staticmethod
     def backward(ctx: Context, grad_output: Tensor) -> Tensor:
         # TODO: Implement for Task 2.4.
-        raise NotImplementedError('Need to implement for Task 2.4')
+        raise NotImplementedError("Need to implement for Task 2.4")
 
 
 class ReLU(Function):
     @staticmethod
     def forward(ctx: Context, t1: Tensor) -> Tensor:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
     @staticmethod
     def backward(ctx: Context, grad_output: Tensor) -> Tensor:
         # TODO: Implement for Task 2.4.
-        raise NotImplementedError('Need to implement for Task 2.4')
+        raise NotImplementedError("Need to implement for Task 2.4")
 
 
 class Log(Function):
     @staticmethod
     def forward(ctx: Context, t1: Tensor) -> Tensor:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
     @staticmethod
     def backward(ctx: Context, grad_output: Tensor) -> Tensor:
         # TODO: Implement for Task 2.4.
-        raise NotImplementedError('Need to implement for Task 2.4')
+        raise NotImplementedError("Need to implement for Task 2.4")
 
 
 class Exp(Function):
     @staticmethod
     def forward(ctx: Context, t1: Tensor) -> Tensor:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
     @staticmethod
     def backward(ctx: Context, grad_output: Tensor) -> Tensor:
         # TODO: Implement for Task 2.4.
-        raise NotImplementedError('Need to implement for Task 2.4')
+        raise NotImplementedError("Need to implement for Task 2.4")
 
 
 class Sum(Function):
@@ -181,43 +181,43 @@ class LT(Function):
     @staticmethod
     def forward(ctx: Context, a: Tensor, b: Tensor) -> Tensor:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
     @staticmethod
     def backward(ctx: Context, grad_output: Tensor) -> Tuple[Tensor, Tensor]:
         # TODO: Implement for Task 2.4.
-        raise NotImplementedError('Need to implement for Task 2.4')
+        raise NotImplementedError("Need to implement for Task 2.4")
 
 
 class EQ(Function):
     @staticmethod
     def forward(ctx: Context, a: Tensor, b: Tensor) -> Tensor:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
     @staticmethod
     def backward(ctx: Context, grad_output: Tensor) -> Tuple[Tensor, Tensor]:
         # TODO: Implement for Task 2.4.
-        raise NotImplementedError('Need to implement for Task 2.4')
+        raise NotImplementedError("Need to implement for Task 2.4")
 
 
 class IsClose(Function):
     @staticmethod
     def forward(ctx: Context, a: Tensor, b: Tensor) -> Tensor:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
 
 class Permute(Function):
     @staticmethod
     def forward(ctx: Context, a: Tensor, order: Tensor) -> Tensor:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
     @staticmethod
     def backward(ctx: Context, grad_output: Tensor) -> Tuple[Tensor, float]:
         # TODO: Implement for Task 2.4.
-        raise NotImplementedError('Need to implement for Task 2.4')
+        raise NotImplementedError("Need to implement for Task 2.4")
 
 
 class View(Function):
diff --git a/minitorch/tensor_ops.py b/minitorch/tensor_ops.py
index db82d54..afdcc54 100644
--- a/minitorch/tensor_ops.py
+++ b/minitorch/tensor_ops.py
@@ -2,26 +2,29 @@
 
 from typing import TYPE_CHECKING, Callable, Optional, Type
 
-import numpy as np
+import numpy as np  # noqa: F401
 from typing_extensions import Protocol
 
 from . import operators
+from .tensor_data import MAX_DIMS  # noqa: F401
+from .tensor_data import broadcast_index  # noqa: F401
+from .tensor_data import index_to_position  # noqa: F401
+from .tensor_data import to_index  # noqa: F401
 from .tensor_data import (
-    MAX_DIMS,
-    broadcast_index,
-    index_to_position,
     shape_broadcast,
-    to_index,
 )
 
 if TYPE_CHECKING:
     from .tensor import Tensor
-    from .tensor_data import Index, Shape, Storage, Strides
+    from .tensor_data import Index  # noqa: F401
+    from .tensor_data import Shape, Storage, Strides
 
 
+# fmt: off
 class MapProto(Protocol):
     def __call__(self, x: Tensor, out: Optional[Tensor] = ..., /) -> Tensor:
         ...
+# fmt: on
 
 
 class TensorOps:
@@ -265,7 +268,7 @@ def _map(
         in_strides: Strides,
     ) -> None:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
     return _map
 
@@ -310,7 +313,7 @@ def _zip(
         b_strides: Strides,
     ) -> None:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
     return _zip
 
@@ -341,7 +344,7 @@ def _reduce(
         reduce_dim: int,
     ) -> None:
         # TODO: Implement for Task 2.3.
-        raise NotImplementedError('Need to implement for Task 2.3')
+        raise NotImplementedError("Need to implement for Task 2.3")
 
     return _reduce
 
diff --git a/project/interface/mlprimer.py b/project/interface/mlprimer.py
index 1d98deb..4cdf608 100644
--- a/project/interface/mlprimer.py
+++ b/project/interface/mlprimer.py
@@ -1,18 +1,17 @@
 import random
 
 import chalk as ch
+from chalk import path  # noqa: F401
 from chalk import (
     Trail,
     empty,
     make_path,
-    path,
     place_on_path,
     rectangle,
     unit_x,
     unit_y,
 )
 from colour import Color
-from drawing import aqua, black, lightblue, lightred
 
 import minitorch
 
@@ -44,11 +43,16 @@ def circle_mark():
         ch.Trail.circle(2).centered().to_path()
         + ch.Trail.circle(1.0, False).centered().to_path()
     )
-    return d.stroke().fill_color(aqua).line_width(0.2).scale_uniform_to_x(0.1)
+    return d.stroke().fill_color(Color("acqua")).line_width(0.2).scale_uniform_to_x(0.1)
 
 
 def origin():
-    return ch.rectangle(1, 1).translate(0.5, -0.5).fill_color(white).line_color(white)
+    return (
+        ch.rectangle(1, 1)
+        .translate(0.5, -0.5)
+        .fill_color(Color("white"))
+        .line_color(Color("white"))
+    )
 
 
 def axes():
@@ -78,7 +82,10 @@ def d_mark():
         True,
     )
     return (
-        t.rotate_by(0.25 / 2).line_width(0.2).scale_uniform_to_x(0.1).fill_color(blue)
+        t.rotate_by(0.25 / 2)
+        .line_width(0.2)
+        .scale_uniform_to_x(0.1)
+        .fill_color(Color("blue"))
     )
 
 
@@ -117,7 +124,7 @@ def x_mark():
 
 
 def points(m, pts):
-    return place_on_path([m] * len(pts), Path.from_list_of_tuples(pts).reflect_y())
+    return place_on_path([m] * len(pts), path.from_list_of_tuples(pts).reflect_y())
 
 
 def draw_below(fn):
@@ -150,19 +157,19 @@ def q(tl, s):
                 q(tl + s * unit_y, s) | q(tl + s * (unit_y + unit_x), s)
             )
 
-    return q(P2(0, 0), 1)
+    return q(P2(0, 0), 1)  # noqa: F821
 
 
-def draw_graph(f, c1=lightred, c2=lightblue):
+def draw_graph(f, c1=Color("red"), c2=Color("lightblue")):
     return quad(lambda x1, x2: f.forward(x1, x2) > 0, c1, c2).reflect_y() + axes()
 
 
 def compare(m1, m2):
     return (
         draw_graph(m1).center_xy()
-        | hstrut(0.5)
-        | text("→", 0.5).fill_color(black)
-        | hstrut(0.5)
+        | hstrut(0.5)  # noqa: F821
+        | text("→", 0.5).fill_color(Color("black"))  # noqa: F821
+        | hstrut(0.5)  # noqa: F821
         | draw_graph(m2).center_xy()
     )
 
@@ -170,12 +177,12 @@ def compare(m1, m2):
 def with_points(pts1, pts2, b):
     "Draw a picture showing line to boundary"
     w1, w2 = 1, 1
-    model = Linear(w1, w2, b)
+    model = Linear(w1, w2, b)  # noqa: F821
     line = make_path([(0, b), (1, b + 1)])
     dia = draw_graph(model) + split_graph(pts1, pts2, False)
 
     for pt in pts1:
-        pt2 = line.get_trace().trace_p(P2(pt[0], -pt[1]), V2(-1, 1))
+        pt2 = line.get_trace().trace_p(P2(pt[0], -pt[1]), V2(-1, 1))  # noqa: F821
         if pt2:
             dia += make_path([(pt[0], -pt[1]), pt2]).dashing([5, 5], 0)
     return dia
@@ -209,22 +216,26 @@ def show_loss(full_loss):
     i = 0
     for j, b in enumerate(range(20)):
         b = -1.7 + b / 20
-        m = Linear(1, 1, b)
+        m = Linear(1, 1, b)  # noqa: F821
         pt = (b, full_loss(m))
         path.append(pt)
         if j % 5 == 0:
-            d = d | hstrut(0.5) | show(m).named(("graph", i))
-            p = circle(0.01).translate(pt[0], pt[1]).fill_color(black)
+            d = d | hstrut(0.5) | show(m).named(("graph", i))  # noqa: F821
+            p = (
+                circle(0.01)  # noqa: F821
+                .translate(pt[0], pt[1])
+                .fill_color(Color("black"))
+            )
             p = p.named(("x", i))
             i += 1
             scores.append(p)
     d = (
-        (concat(scores) + make_path(path)).center_xy().scale(3)
-        / vstrut(0.5)
+        (concat(scores) + make_path(path)).center_xy().scale(3)  # noqa: F821
+        / vstrut(0.5)  # noqa: F821
         / d.scale(2).center_xy()
     )
     for i in range(i):
-        d = d.connect(("graph", i), ("x", i), ArrowOpts(head_pad=0.1))
+        d = d.connect(("graph", i), ("x", i), ArrowOpts(head_pad=0.1))  # noqa: F821
     return d
 
 
diff --git a/project/run_manual.py b/project/run_manual.py
index 302846f..d14c780 100644
--- a/project/run_manual.py
+++ b/project/run_manual.py
@@ -2,6 +2,7 @@
 Be sure you have minitorch installed in you Virtual Env.
 >>> pip install -Ue .
 """
+
 import random
 
 import minitorch
diff --git a/project/run_scalar.py b/project/run_scalar.py
index 4b7ee22..cad6511 100644
--- a/project/run_scalar.py
+++ b/project/run_scalar.py
@@ -2,6 +2,7 @@
 Be sure you have minitorch installed in you Virtual Env.
 >>> pip install -Ue .
 """
+
 import random
 
 import minitorch
diff --git a/project/run_sentiment.py b/project/run_sentiment.py
index 4da963d..8202062 100644
--- a/project/run_sentiment.py
+++ b/project/run_sentiment.py
@@ -105,7 +105,7 @@ def get_predictions_array(y_true, model_output):
 
 def get_accuracy(predictions_array):
     correct = 0
-    for (y_true, y_pred, logit) in predictions_array:
+    for y_true, y_pred, logit in predictions_array:
         if y_true == y_pred:
             correct += 1
     return correct / len(predictions_array)
diff --git a/requirements.txt b/requirements.txt
index 40efede..a7b3763 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,12 @@
 colorama==0.4.3
-hypothesis == 6.54
-mypy == 0.971
-numba == 0.56
-numpy == 1.22
-pre-commit == 2.20.0
-pytest == 7.1.2
+hypothesis==6.54
+mypy==0.971
+numba==0.56
+numpy==1.22
+pre-commit==2.20.0
+pytest==7.1.2
 pytest-env
-pytest-runner == 5.2
+pytest-runner==5.2
 typing_extensions
+colour==0.1.5
+chalk-diagrams @ git+https://github.com/chalk-diagrams/chalk
\ No newline at end of file
diff --git a/tests/test_nn.py b/tests/test_nn.py
index 23f9b3e..9c2efe9 100644
--- a/tests/test_nn.py
+++ b/tests/test_nn.py
@@ -32,7 +32,7 @@ def test_avg(t: Tensor) -> None:
 @given(tensors(shape=(2, 3, 4)))
 def test_max(t: Tensor) -> None:
     # TODO: Implement for Task 4.4.
-    raise NotImplementedError('Need to implement for Task 4.4')
+    raise NotImplementedError("Need to implement for Task 4.4")
 
 
 @pytest.mark.task4_4
diff --git a/tests/test_operators.py b/tests/test_operators.py
index 1069423..8e9886c 100644
--- a/tests/test_operators.py
+++ b/tests/test_operators.py
@@ -5,6 +5,7 @@
 from hypothesis.strategies import lists
 
 from minitorch import MathTest
+from minitorch.operators import sigmoid  # noqa: F401
 from minitorch.operators import (
     add,
     addLists,
@@ -21,7 +22,6 @@
     prod,
     relu,
     relu_back,
-    sigmoid,
     sum,
 )
 
@@ -108,7 +108,7 @@ def test_sigmoid(a: float) -> None:
     * It is  strictly increasing.
     """
     # TODO: Implement for Task 0.2.
-    raise NotImplementedError('Need to implement for Task 0.2')
+    raise NotImplementedError("Need to implement for Task 0.2")
 
 
 @pytest.mark.task0_2
@@ -116,7 +116,7 @@ def test_sigmoid(a: float) -> None:
 def test_transitive(a: float, b: float, c: float) -> None:
     "Test the transitive property of less-than (a < b and b < c implies a < c)"
     # TODO: Implement for Task 0.2.
-    raise NotImplementedError('Need to implement for Task 0.2')
+    raise NotImplementedError("Need to implement for Task 0.2")
 
 
 @pytest.mark.task0_2
@@ -126,7 +126,7 @@ def test_symmetric() -> None:
     gives the same value regardless of the order of its input.
     """
     # TODO: Implement for Task 0.2.
-    raise NotImplementedError('Need to implement for Task 0.2')
+    raise NotImplementedError("Need to implement for Task 0.2")
 
 
 @pytest.mark.task0_2
@@ -136,7 +136,7 @@ def test_distribute() -> None:
     :math:`z \times (x + y) = z \times x + z \times y`
     """
     # TODO: Implement for Task 0.2.
-    raise NotImplementedError('Need to implement for Task 0.2')
+    raise NotImplementedError("Need to implement for Task 0.2")
 
 
 @pytest.mark.task0_2
@@ -145,7 +145,7 @@ def test_other() -> None:
     Write a test that ensures some other property holds for your functions.
     """
     # TODO: Implement for Task 0.2.
-    raise NotImplementedError('Need to implement for Task 0.2')
+    raise NotImplementedError("Need to implement for Task 0.2")
 
 
 # ## Task 0.3  - Higher-order functions
@@ -174,7 +174,7 @@ def test_sum_distribute(ls1: List[float], ls2: List[float]) -> None:
     is the same as the sum of each element of `ls1` plus each element of `ls2`.
     """
     # TODO: Implement for Task 0.3.
-    raise NotImplementedError('Need to implement for Task 0.3')
+    raise NotImplementedError("Need to implement for Task 0.3")
 
 
 @pytest.mark.task0_3