[ruff] Enable pydoc rules (#3288)

### Changes

Enable rule https://docs.astral.sh/ruff/rules/#pydocstyle-d

ignored rules:
```
  "D100", # undocumented-public-module
  "D101", # undocumented-public-class
  "D102", # undocumented-public-method
  "D103", # undocumented-public-function
  "D104", # undocumented-public-package
  "D105", # undocumented-magic-method
  "D106", # undocumented-public-nested-class
  "D107", # undocumented-public-init
  "D200", # unnecessary-multiline-docstring
  "D203", # incorrect-blank-line-before-class
  "D205", # missing-blank-line-after-summary
  "D212", # multi-line-summary-first-line
  "D400", # missing-trailing-period
  "D401", # non-imperative-mood
  "D402", # signature-in-docstring
  "D404", # docstring-starts-with-this
  "D413", # missing-blank-line-after-last-section
  "D415", # missing-terminal-punctuation
  "D417", # undocumented-param
```
Some rules is not supported because used not supported style or require
add or change documentation in too many files

Author: AlexanderDokuchaev

nncf/common/composite_compression.py

@@ -75,7 +75,6 @@ def calculate(self, *args: Any, **kwargs: Any) -> Any:
 
         :return: The compression loss value.
         """
-
         if len(self._child_losses) == 0:
             msg = "Cannot calculate the loss value because the number of child loss is 0."
             raise nncf.InternalError(msg)

nncf/common/graph/graph.py

@@ -343,7 +343,6 @@ def get_previous_nodes(self, node: NNCFNode) -> List[NNCFNode]:
         :param node: Consumer node.
         :return: List of producers nodes of provided node.
         """
-
         nx_node_keys = self._nx_graph.pred[self._node_id_to_key_dict[node.node_id]]
         return [self._nodes[key] for key in nx_node_keys]
 
@@ -723,7 +722,6 @@ def get_nncf_graph_pattern_io(self, match: List[str]) -> NNCFGraphPatternIO:
         `match` list
         :return: NNCFGraphPatternIO object describing the inputs and outputs of the matched subgraph
         """
-
         in_edge_boundary, out_edge_boundary = NNCFGraph._get_edge_boundaries(match, self._nx_graph)
         boundary = in_edge_boundary + out_edge_boundary
         input_nncf_edges = []

nncf/common/graph/graph_matching.py

@@ -57,6 +57,7 @@ def _is_subgraph_matching_strict(graph: nx.DiGraph, pattern: nx.DiGraph, subgrap
     3) External successors or predecessors of the nodes which are not starting and last.
     If any of these conditions is True, than returns False, otherwise - True.
     The checks are skipped for NON_PATTERN_NODE_TYPE.
+
     Example:
     This subgraph matching is not strict.
     (conv2d + BN + ReLU pattern):
@@ -71,6 +72,7 @@ def _is_subgraph_matching_strict(graph: nx.DiGraph, pattern: nx.DiGraph, subgrap
            (cat)----/
              |
             ...
+
     :param graph: The model graph.
     :param pattern: The matched pattern.
     :param subgraph: A subgraph of the model graph including the nodes outside the pattern.

nncf/common/graph/patterns/patterns.py

@@ -101,7 +101,6 @@ def __add__(self, other: "GraphPattern") -> "GraphPattern":
         :param other: GraphPattern that will be added.
         :return: resulted GraphPattern.
         """
-
         final_pattern = GraphPattern()
         for self_subgraph in self.get_weakly_connected_subgraphs():
             for other_subgraph in other.get_weakly_connected_subgraphs():

nncf/common/insertion_point_graph.py

@@ -87,7 +87,6 @@ def __init__(
         If left unspecified, every node in `nncf_graph` will be allowed to have a single post-hook for its output
          (post-hooking separate tensors in an operation's output is not currently supported)
         """
-
         super().__init__()
         self._base_nx_graph = deepcopy(nncf_graph.get_nx_graph_copy())
 
@@ -320,7 +319,6 @@ def get_ip_graph_with_merged_hw_optimized_operations(
         :param full_fusing_pattern: The GraphPatttern object representing a composition of fusing pattern variants.
         :return: The InsertionPointGraph with nodes fused according to pattern matching.
         """
-
         merged_ip_graph = deepcopy(self)
         matches = find_subgraphs_matching_pattern(merged_ip_graph.get_base_nx_graph(), full_fusing_pattern)
         for match in matches:

nncf/common/logging/track_progress.py

@@ -179,7 +179,6 @@ def __init__(
             it takes to process sequence elements. Useful when processing time is strongly non-uniform.
         :return: An iterable of the values in the sequence.
         """
-
         self.sequence = sequence
         self.weights = weights
         self.total = sum(self.weights) if self.weights is not None else total

nncf/common/pruning/mask_propagation.py

@@ -94,7 +94,6 @@ def symbolic_mask_propagation(
             are supported by the NNCF pruning algorithm.
         :return: Dict of node indices vs the decision made by symbolic mask propagation algorithm.
         """
-
         can_be_closing_convs = self._get_can_closing_convs(prunable_layers_types)
         can_prune_by_dim: Dict[int, PruningAnalysisDecision] = {k: None for k in can_be_closing_convs}  # type: ignore
         for node in self._graph.topological_sort():

nncf/common/pruning/node_selector.py

@@ -91,7 +91,6 @@ def create_pruning_groups(self, graph: NNCFGraph) -> Clusterization[NNCFNode]:
         :param graph: Graph to work with and their initialization parameters as values.
         :return: Clusterization of pruned nodes.
         """
-
         all_nodes_to_prune = graph.get_nodes_by_types(self._prune_operations_types)  # NNCFNodes here
 
         # 1. Clusters for special ops
@@ -218,7 +217,6 @@ def _pruning_dimensions_analysis(
             are supported by the NNCF pruning algorithm
         :return: Pruning node analysis after model analyzer, pruning algo compatibility and pruning dimensions checks.
         """
-
         nodes_of_group_with_non_eq_pruning_dim = self._check_internal_groups_dim(pruned_nodes_clusterization)
         can_prune_after_check_updated = can_prune_after_check.copy()
         for node_id, val in nodes_of_group_with_non_eq_pruning_dim.items():

nncf/common/quantization/initialization/range.py

@@ -87,7 +87,6 @@ def __init__(
             specified type of range initialization will be applied. It can be
             quantizers group for activations or weights.
         """
-
         super().__init__(
             range_init_config.init_type, range_init_config.num_init_samples, range_init_config.init_type_specific_params
         )

nncf/common/quantization/quantizer_propagation/graph.py

@@ -797,7 +797,6 @@ def all_outputs_are_quantized(self, node_key: str) -> bool:
         :return: True if all paths from the given node to the first
         input quantizable nodes have an activation quantizer, False otherwise.
         """
-
         nodes_keys_stack = deque(self.successors(node_key))
         while nodes_keys_stack:
             node_key = nodes_keys_stack.popleft()
@@ -1424,7 +1423,6 @@ def _handle_output_quantizers_for_weights_as_outputs_ops(
         :return: A MultiConfigQuantizerSetup with weights-as-outputs-dependent quantizers removed where possible
             and shared inputs/unified scales group adjusted to reflect the change.
         """
-
         # For the weights-are-outputs quantized operations, need to find out the dependent activation quantizers in
         # the multiconfig setup and see if it is possible to avoid requantization by selecting a common configuration
         # subset. If yes and the activation quantizer becomes unnecessary, need to unify the scales of the weight