fix imports

src/mygrad/_utils/__init__.py

@@ -17,8 +17,8 @@
 from weakref import ReferenceType
 
 if TYPE_CHECKING:  # pragma: no cover
-    from mygrad import Tensor
     from mygrad.operation_base import Operation  # noqa: F401
+    from mygrad.tensor_base import Tensor
 
 __all__ = [
     "collect_all_operations_and_clear_grads",

src/mygrad/_utils/duplicating_graph.py

@@ -18,7 +18,7 @@
 from mygrad.operation_base import Operation
 
 if TYPE_CHECKING:  # pragma: no cover
-    from mygrad import Tensor
+    from mygrad.tensor_base import Tensor
 
 
 T = TypeVar("T")

src/mygrad/_utils/lock_management.py

@@ -4,23 +4,17 @@
 
 import os
 from collections import Counter, defaultdict
-from typing import (
-    TYPE_CHECKING,
-    Counter as CounterType,
-    DefaultDict,
-    Dict,
-    Generator,
-    Iterable,
-    Set,
-)
+from typing import TYPE_CHECKING
+from typing import Counter as CounterType
+from typing import DefaultDict, Dict, Generator, Iterable, Set
 from weakref import finalize, ref
 
 import numpy as np
 
 from mygrad._utils import ContextTracker, WeakRef, WeakRefIterable
 
 if TYPE_CHECKING:  # pragma: no cover
-    from mygrad import Tensor as TensorType
+    from mygrad.tensor_base import Tensor as TensorType
 
 
 # arr-id -> num active ops involving arr

src/mygrad/math/arithmetic/ops.py

@@ -6,7 +6,7 @@
 from mygrad.operation_base import BinaryUfunc, Operation, UnaryUfunc
 
 if TYPE_CHECKING:  # pragma: no cover
-    from mygrad import Tensor
+    from mygrad.tensor_base import Tensor
 
 __all__ = [
     "Add",

src/mygrad/nnet/activations/elu.py

@@ -3,8 +3,8 @@
 
 import numpy as np
 
-from mygrad import Tensor
 from mygrad.operation_base import Operation
+from mygrad.tensor_base import Tensor
 from mygrad.typing import ArrayLike
 
 __all__ = ["elu"]

src/mygrad/nnet/activations/glu.py

@@ -2,7 +2,8 @@
 
 from numpy import ndarray
 
-from mygrad import Tensor, multiply
+from mygrad.math.arithmetic.funcs import multiply
+from mygrad.tensor_base import Tensor
 from mygrad.typing import ArrayLike
 
 from .sigmoid import sigmoid

src/mygrad/nnet/activations/selu.py

@@ -2,8 +2,8 @@
 
 import numpy as np
 
-from mygrad import Tensor
 from mygrad.operation_base import Operation
+from mygrad.tensor_base import Tensor
 from mygrad.typing import ArrayLike
 
 __all__ = ["selu"]

src/mygrad/nnet/initializers/dirac.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from mygrad import Tensor
+from mygrad.tensor_base import Tensor
 
 
 def dirac(*shape: int, dtype=np.float32, constant: Optional[bool] = None) -> Tensor:

src/mygrad/nnet/initializers/glorot_normal.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from mygrad import Tensor
+from mygrad.tensor_base import Tensor
 
 
 def glorot_normal(*shape, gain=1, dtype=np.float32, constant=None):

src/mygrad/nnet/initializers/normal.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from mygrad import Tensor
+from mygrad.tensor_base import Tensor
 
 
 def normal(*shape, mean=0, std=1, dtype=np.float32, constant=None):

src/mygrad/nnet/initializers/uniform.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from mygrad import Tensor
+from mygrad.tensor_base import Tensor
 
 
 def uniform(*shape, lower_bound=0, upper_bound=1, dtype=np.float32, constant=None):

src/mygrad/nnet/layers/batchnorm.py

@@ -2,8 +2,8 @@
 
 import numpy as np
 
-from mygrad import Tensor
 from mygrad.operation_base import Operation
+from mygrad.tensor_base import Tensor
 from mygrad.typing import ArrayLike
 
 __all__ = ["batchnorm"]
@@ -114,7 +114,7 @@ def batchnorm(
     gamma: Optional[ArrayLike] = None,
     beta: Optional[ArrayLike] = None,
     eps: float,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     """
     Performs batch normalization on ``x``::

src/mygrad/nnet/losses/negative_log_likelihood.py

@@ -2,7 +2,8 @@
 
 import numpy as np
 
-from mygrad import Tensor, asarray, mean
+from mygrad.math.sequential.funcs import mean
+from mygrad.tensor_base import Tensor, asarray
 from mygrad.typing import ArrayLike
 
 from ._utils import check_loss_inputs

src/mygrad/operation_base.py

@@ -14,7 +14,7 @@
 from mygrad.typing import DTypeLike, Mask
 
 if TYPE_CHECKING:  # pragma: no cover
-    from mygrad import Tensor
+    from mygrad.tensor_base import Tensor
 
 
 __all__ = [

src/mygrad/random/funcs.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from mygrad import Tensor
+from mygrad.tensor_base import Tensor
 from mygrad.typing import Shape