Refactor type casting in Cython files for improved clarity and consistency

- Updated various Cython files to explicitly cast types, enhancing type safety and readability.
- Adjusted the `engine_name` property in `InferenceEngine` and its subclasses to be set directly in the constructor.
- Modified the `request` method in `_SessionWithBase` to accept `*args` for better flexibility.
- Ensured proper type casting for return values in methods across multiple classes, including `Inference`, `CoreMLEngine`, and `TensorRTEngine`.

These changes aim to streamline the codebase and improve maintainability by enforcing consistent type usage.

engines/tensorrt_engine.pyx

@@ -1,7 +1,7 @@
 from engines.inference_engine cimport InferenceEngine
-import tensorrt as trt
-import pycuda.driver as cuda
-import pycuda.autoinit  # required for automatically initialize CUDA, do not remove.
+import tensorrt as trt  # pyright: ignore[reportMissingImports]
+import pycuda.driver as cuda  # pyright: ignore[reportMissingImports]
+import pycuda.autoinit  # pyright: ignore[reportMissingImports]  # required for automatically initialize CUDA, do not remove.
 import pynvml
 import numpy as np
 cimport constants_inf
@@ -54,6 +54,7 @@ cdef class TensorRTEngine(InferenceEngine):
 
         except Exception as e:
             raise RuntimeError(f"Failed to initialize TensorRT engine: {str(e)}")
+        self.engine_name = <str>"tensorrt"
 
     @staticmethod
     def get_gpu_memory_bytes(int device_id):
@@ -72,10 +73,6 @@ cdef class TensorRTEngine(InferenceEngine):
                 pass
         return 2 * 1024 * 1024 * 1024 if total_memory is None else total_memory # default 2 Gb
 
-    @property
-    def engine_name(self):
-        return "tensorrt"
-
     @staticmethod
     def get_engine_filename():
         try:
@@ -123,23 +120,22 @@ cdef class TensorRTEngine(InferenceEngine):
             return bytes(plan)
 
     cdef tuple get_input_shape(self):
-        return self.input_shape[2], self.input_shape[3]
+        return <tuple>(self.input_shape[2], self.input_shape[3])
 
     cdef int get_batch_size(self):
-        return self.batch_size
+        return <int>self.batch_size
 
     cdef run(self, input_data):
         try:
             cuda.memcpy_htod_async(self.d_input, input_data, self.stream)
-            self.context.set_tensor_address(self.input_name, int(self.d_input))  # input buffer
-            self.context.set_tensor_address(self.output_name, int(self.d_output))  # output buffer
+            self.context.set_tensor_address(self.input_name, int(self.d_input))  # type: ignore
+            self.context.set_tensor_address(self.output_name, int(self.d_output))  # type: ignore
 
-            self.context.execute_async_v3(stream_handle=self.stream.handle)
-            self.stream.synchronize()
+            self.context.execute_async_v3(stream_handle=self.stream.handle)  # type: ignore
+            self.stream.synchronize()  # type: ignore
 
-            # Fix: Remove the stream parameter from memcpy_dtoh
             cuda.memcpy_dtoh(self.h_output, self.d_output)
-            output = self.h_output.reshape(self.output_shape)
+            output = self.h_output.reshape(self.output_shape)  # type: ignore
             return [output]
 
         except Exception as e: