docs(mge): fix docstring of cgtools

GitOrigin-RevId: ee8c93b9f7d4f4841b7690c3c764b41ec9f93dc1

imperative/python/megengine/core/tensor/function.py

@@ -66,10 +66,10 @@ class Function:
 
         :param output_grads: gradients of outputs that are returned by :meth:`~.function.Function.forward`.
 
-            .. note::
+        .. note::
 
-                In case when some tensors of outputs are not related to loss function, the corresponding
-                values in ``output_grads`` would be ``None``.
+            In case when some tensors of outputs are not related to loss function, the corresponding
+            values in ``output_grads`` would be ``None``.
 
         .. note::
 

imperative/python/megengine/functional/elemwise.py

@@ -225,8 +225,8 @@ def square(x: Tensor) -> Tensor:
     """
     Returns a new tensor with the square of the elements of input tensor.
 
-    :param inp: The input tensor
-    :return: The computed tensor
+    :param inp: input tensor.
+    :return: computed tensor.
 
     Examples:
 

imperative/python/megengine/functional/math.py

@@ -72,7 +72,7 @@ def isinf(inp: Tensor) -> Tensor:
     r"""Returns a new tensor representing if each element is ``Inf`` or not.
 
     :param inp: input tensor.
-    :return: c.
+    :return: result tensor.
 
     Examples:
 

imperative/python/megengine/serialization.py

@@ -88,9 +88,9 @@ def load(f, map_location=None, pickle_module=pickle):
     :type map_location: str, dict or a function specifying the map rules
     :param map_location: Default: ``None``.
 
-        .. note::
+    .. note::
 
-            map_location defines device mapping. See examples for usage.
+        map_location defines device mapping. See examples for usage.
 
     :type pickle_module:
     :param pickle_module: Default: ``pickle``.

imperative/python/megengine/utils/comp_graph_tools.py

@@ -17,8 +17,8 @@ from ..core.tensor.raw_tensor import as_raw_tensor
 
 
 def get_dep_vars(var: VarNode, var_type: str = None) -> List[VarNode]:
-    """return :class:`.tensor.core.megbrain_graph.VarNode` of type ``var_type`` that input ``var``
-    depands on. If ``var_type`` is None, return all types.
+    """Returns :class:`.tensor.core.megbrain_graph.VarNode` of type ``var_type`` that input ``var``
+    depands on. If ``var_type`` is None, returns all types.
     """
     outputs = []
     memo = set()
@@ -46,14 +46,14 @@ def get_dep_vars(var: VarNode, var_type: str = None) -> List[VarNode]:
 
 
 def get_owner_opr_inputs(var: VarNode) -> List[VarNode]:
-    """get the inputs of owner opr of a variable
+    """Gets the inputs of owner opr of a variable.
     """
     assert isinstance(var, VarNode)
     return var.owner.inputs
 
 
 def get_owner_opr_type(var: VarNode) -> str:
-    """get the type of owner opr of a variable
+    """Gets the type of owner opr of a variable.
 
     """
     assert isinstance(var, VarNode)
@@ -61,16 +61,16 @@ def get_owner_opr_type(var: VarNode) -> str:
 
 
 def get_opr_type(opr: OperatorNode) -> str:
-    """get the type of a opr
+    """Gets the type of an opr.
     """
     assert isinstance(opr, OperatorNode)
     return opr.type
 
 
 def graph_traversal(outputs: VarNode):
-    """helper function to traverse the computing graph and return enough useful information
+    """Helper function to traverse the computing graph and return enough useful information.
 
-    :param outputs: model outputs
+    :param outputs: model outputs.
     :return:  tuple (map_oprs, map_vars, var2oprs, opr2receivers, indegree2opr, opr2indegree)
         WHERE
         map_oprs is dict from opr_id to actual opr
@@ -124,11 +124,11 @@ def graph_traversal(outputs: VarNode):
 
 
 def get_oprs_seq(outputs: List[VarNode], prune_reshape=False) -> List[OperatorNode]:
-    """get oprs in some topological order for a dumped model
+    """Gets oprs in some topological order for a dumped model.
 
-    :param outputs: model outputs
-    :param prune_reshape: whether to prune the operators useless during inference
-    :return: opr list with some correct execution order
+    :param outputs: model outputs.
+    :param prune_reshape: whether to prune the useless operators during inference.
+    :return: opr list with some correct execution order.
     """
 
     def topological_sort(map_oprs, opr2receivers, indegree2opr, opr2indegree):
@@ -194,13 +194,13 @@ def get_oprs_seq(outputs: List[VarNode], prune_reshape=False) -> List[OperatorNo
 
 
 def replace_vars(dst: VarNode, varmap: Dict[VarNode, VarNode]) -> List[VarNode]:
-    """replace vars in the graph
+    """Replaces vars in the graph.
 
-    :param dst: target vars representing the graph
-    :param varmap: the map that specifies how to replace the vars
+    :param dst: target vars representing the graph.
+    :param varmap: the map that specifies how to replace the vars.
 
     :return: new vars that correspond to ``dst`` with all the dependencies
-        replaced
+        replaced.
     """
     dst_vec = []
     repl_src_vec = []
@@ -221,13 +221,13 @@ def replace_vars(dst: VarNode, varmap: Dict[VarNode, VarNode]) -> List[VarNode]:
 def replace_oprs(
     dst: List[VarNode], oprmap: Dict[OperatorNode, OperatorNode]
 ) -> List[VarNode]:
-    """Replace operators in the graph.
+    """Replaces operators in the graph.
 
-    :param dst: target vars representing the graph
-    :param oprmap: the map that specifies how to replace the operators
+    :param dst: target vars representing the graph.
+    :param oprmap: the map that specifies how to replace the operators.
 
     :return: new vars that correspond to ``dst`` with all the dependencies
-        replaced
+        replaced.
     """
     dst_vec = []
     repl_src_vec = []
@@ -246,9 +246,9 @@ def replace_oprs(
 
 
 def set_priority_to_id(dest_vars):
-    """For all oprs in the subgraph constructed by dest_vars
-       set its priority to id if its original priority is zero
-    :param dest_vars: target vars representing the graph
+    """For all oprs in the subgraph constructed by dest_vars,
+       sets its priority to id if its original priority is zero.
+    :param dest_vars: target vars representing the graph.
     """
     dest_vec = []
     for i in dest_vars:
@@ -258,11 +258,11 @@ def set_priority_to_id(dest_vars):
 
 
 def load_and_inference(file, inp_data_list: List[numpy.ndarray]) -> List[numpy.ndarray]:
-    """Load a serialized computing graph and run inference with input data.
+    """Loads a serialized computing graph and run inference with input data.
 
-    :param file: Path or Handle of the input file.
-    :param inp_data_list: List of input data.
-    :return: List of inference results.
+    :param file: path or handle of the input file.
+    :param inp_data_list: list of input data.
+    :return: list of inference results.
 
     """
     *_, out_list = G.load_graph(file)