Do not use old `T` alias in documentation.

doc/library/scan.rst

@@ -45,7 +45,7 @@ The equivalent Aesara code would be:
 
   # Symbolic description of the result
   result, updates = aesara.scan(fn=lambda prior_result, A: prior_result * A,
-                                outputs_info=T.ones_like(A),
+                                outputs_info=aet.ones_like(A),
                                 non_sequences=A,
                                 n_steps=k)
 
@@ -174,7 +174,7 @@ downcast** of the latter.
     seq = aet.arange(up_to)
 
     # An unauthorized implicit downcast from the dtype of 'seq', to that of
-    # 'T.as_tensor_variable(0)' which is of dtype 'int8' by default would occur
+    # 'aet.as_tensor_variable(0)' which is of dtype 'int8' by default would occur
     # if this instruction were to be used instead of the next one:
     # outputs_info = aet.as_tensor_variable(0)
 

doc/tutorial/examples.rst

@@ -562,7 +562,7 @@ It will be used repeatedly.
     print(b.get_value())
 
     # Construct Aesara expression graph
-    p_1 = 1 / (1 + aet.exp(-T.dot(x, w) - b))        # Probability that target = 1
+    p_1 = 1 / (1 + aet.exp(-aet.dot(x, w) - b))        # Probability that target = 1
     prediction = p_1 > 0.5                          # The prediction thresholded
     xent = -y * aet.log(p_1) - (1-y) * aet.log(1-p_1) # Cross-entropy loss function
     cost = xent.mean() + 0.01 * (w ** 2).sum()      # The cost to minimize

doc/tutorial/gradients.rst

@@ -124,7 +124,7 @@ do is to loop over the entries in *y* and compute the gradient of
 >>> import aesara.tensor as aet
 >>> x = aet.dvector('x')
 >>> y = x ** 2
->>> J, updates = aesara.scan(lambda i, y, x : aet.grad(y[i], x), sequences=T.arange(y.shape[0]), non_sequences=[y, x])
+>>> J, updates = aesara.scan(lambda i, y, x : aet.grad(y[i], x), sequences=aet.arange(y.shape[0]), non_sequences=[y, x])
 >>> f = aesara.function([x], J, updates=updates)
 >>> f([4, 4])
 array([[ 8.,  0.],
@@ -163,7 +163,7 @@ scalar.
 >>> y = x ** 2
 >>> cost = y.sum()
 >>> gy = aet.grad(cost, x)
->>> H, updates = aesara.scan(lambda i, gy,x : aet.grad(gy[i], x), sequences=T.arange(gy.shape[0]), non_sequences=[gy, x])
+>>> H, updates = aesara.scan(lambda i, gy,x : aet.grad(gy[i], x), sequences=aet.arange(gy.shape[0]), non_sequences=[gy, x])
 >>> f = aesara.function([x], H, updates=updates)
 >>> f([4, 4])
 array([[ 2.,  0.],