fix whitespaces for type annotations according to pep

Signed-off-by: dchigarev <dmitry.chigarev@intel.com>

Author: dchigarev

python/examples/ingress/torch/01-dummy-mlp-from-file.py

@@ -32,7 +32,7 @@
 # - Loads the DummyMLP class and instantiates it with arguments obtained from 'get_init_inputs()'
 # - Calls get_sample_inputs() to get sample input tensors for shape inference
 # - Converts PyTorch model to linalg-on-tensors dialect operations using torch_mlir
-mlir_module_ir : ir.Module = import_from_file(
+mlir_module_ir: ir.Module = import_from_file(
     model_path,                              # Path to the Python file containing the model
     model_class_name="DummyMLP",             # Name of the PyTorch nn.Module class to convert
     init_args_fn_name="get_init_inputs",     # Function that returns args for model.__init__()
@@ -48,7 +48,7 @@
 # also be done with the MLIR module.
 
 # Step 3: Extract the main function operation from the MLIR module and print its metadata
-func_op : func.FuncOp = mlir_module_ir.operation.regions[0].blocks[0].operations[0]
+func_op: func.FuncOp = mlir_module_ir.operation.regions[0].blocks[0].operations[0]
 print(f"entry-point name: {func_op.name}")
 print(f"entry-point type: {func_op.type}")
 

python/examples/ingress/torch/02-dummy-mlp-from-model.py

@@ -28,7 +28,7 @@
 
 ir_context = ir.Context()
 # Step 2: Convert the PyTorch model to MLIR
-mlir_module_ir : ir.Module = import_from_model(
+mlir_module_ir: ir.Module = import_from_model(
     model,
     sample_args=(sample_input,),
     ir_context=ir_context
@@ -41,7 +41,7 @@
 # also be done with the MLIR module.
 
 # Step 3: Extract the main function operation from the MLIR module and print its metadata
-func_op : func.FuncOp = mlir_module_ir.operation.regions[0].blocks[0].operations[0]
+func_op: func.FuncOp = mlir_module_ir.operation.regions[0].blocks[0].operations[0]
 print(f"entry-point name: {func_op.name}")
 print(f"entry-point type: {func_op.type}")
 

python/lighthouse/ingress/torch/torch_import.py

@@ -27,10 +27,10 @@
 
 def import_from_model(
     model: nn.Module,
-    sample_args : Iterable,
-    sample_kwargs : Mapping = None,
-    dialect : OutputType | str = OutputType.LINALG_ON_TENSORS,
-    ir_context : ir.Context | None = None,
+    sample_args: Iterable,
+    sample_kwargs: Mapping = None,
+    dialect: OutputType | str = OutputType.LINALG_ON_TENSORS,
+    ir_context: ir.Context | None = None,
     **kwargs,
 ) -> str | ir.Module:
     """Import a PyTorch nn.Module into MLIR.
@@ -100,9 +100,9 @@ def import_from_file(
     init_kwargs_fn_name: str | None = None,
     sample_args_fn_name: str = "get_inputs",
     sample_kwargs_fn_name: str | None = None,
-    state_path : str | Path | None = None,
-    dialect : OutputType | str = OutputType.LINALG_ON_TENSORS,
-    ir_context : ir.Context | None = None,
+    state_path: str | Path | None = None,
+    dialect: OutputType | str = OutputType.LINALG_ON_TENSORS,
+    ir_context: ir.Context | None = None,
     **kwargs,
 ) -> str | ir.Module:
     """Load a PyTorch nn.Module from a file and import it into MLIR.
@@ -211,7 +211,7 @@ def get_inputs():
         error_msg=f"Sample kwargs function '{sample_kwargs_fn_name}' not found in {filepath}"
     )
 
-    nn_model : nn.Module = model(*model_init_args, **model_init_kwargs)
+    nn_model: nn.Module = model(*model_init_args, **model_init_kwargs)
     if state_path is not None:
         state_dict = torch.load(state_path)
         nn_model.load_state_dict(state_dict)