more tensor.py docs (#4686)

wow much docs

docs/tensor.md

@@ -24,6 +24,7 @@
 ::: tinygrad.Tensor.shard_
 ::: tinygrad.Tensor.contiguous
 ::: tinygrad.Tensor.contiguous_backward
+::: tinygrad.Tensor.backward
 
 ## Creation (basic)
 

tinygrad/tensor.py

@@ -648,6 +648,17 @@ class Tensor:
     return list(_walk(self, set()))
 
   def backward(self) -> Tensor:
+    """
+    Propagates the gradient of a tensor backwards through the computation graph.
+    Must be used on a scalar tensor.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor.arange(6.0, requires_grad=True)
+    t2 = t.sum()
+    t2.backward()
+    print(t.grad.numpy())
+    ```
+    """
     assert self.shape == tuple(), f"backward can only be called for scalar tensors, but it has shape {self.shape})"
 
     # fill in the first grad with one. don't use Tensor.ones because we don't need contiguous
@@ -668,14 +679,50 @@ class Tensor:
 
   # ***** movement mlops *****
 
-  def view(self, *shape) -> Tensor: return self.reshape(shape)  # in tinygrad, view and reshape are the same thing
+  def view(self, *shape) -> Tensor:
+    """
+    `.view` is an alias for `.reshape`.
+    """
+    return self.reshape(shape)
   def reshape(self, shape, *args) -> Tensor:
+    """
+    Returns a new tensor with the same data as the original tensor but with a different shape.
+    shape can be passed as a tuple or as separate arguments.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor.arange(6)
+    print(t.reshape(2, 3).numpy())
+    ```
+    """
     new_shape = argfix(shape, *args)
     new_shape = tuple([-prod(self.shape) // prod(new_shape) if s == -1 else (s if s is not None else self.shape[i]) for i,s in enumerate(new_shape)])
     return F.Reshape.apply(self, shape=new_shape) if new_shape != self.shape else self
   def expand(self, shape, *args) -> Tensor:
+    """
+    Returns a new tensor that is expanded to the shape that is specified.
+    Expand can also increase the number of dimensions that a tensor has.
+
+    Passing a `-1` or `None` to a dimension means that it's size will not be changed.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor([1, 2, 3])
+    print(t.expand(4, -1).numpy())
+    ```
+    """
     return self._broadcast_to(tuple(sh if s==-1 or s is None else s for s, sh in zip(*(_pad_left(argfix(shape, *args), self.shape)))))
-  def permute(self, order, *args) -> Tensor: return F.Permute.apply(self, order=argfix(order, *args))
+  def permute(self, order, *args) -> Tensor:
+    """
+    Returns a new tensor that is a permutation of the original tensor.
+    The new tensor has the same data as the original tensor but with the dimensions permuted according to the order specified.
+    order can be passed as a tuple or as separate arguments.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor.arange(6).reshape(2, 3)
+    print(t.numpy(), "->")
+    print(t.permute(1, 0).numpy())
+    ```
+    """
+    return F.Permute.apply(self, order=argfix(order, *args))
   def flip(self, axis, *args) -> Tensor: return F.Flip.apply(self, axis=[x if x >= 0 else x+len(self.shape) for x in argfix(axis, *args)])
   def shrink(self, arg:Tuple[Optional[Tuple[sint, sint]], ...]) -> Tensor:
     if all(x is None or x == (0,s) for x,s in zip(arg, self.shape)): return self