TF FE import model from memory (#15242)

* Added import model from memory for TF FE using string.

* Small correction.

* Clang format.

* Code correction.

* Implemented model importing to TF FE using temporary file.

* Removed wrong changes.

* Added check.

* Removed code duplication.

* Corrected logging of cli parameters.

tests/layer_tests/mo_python_api_tests/test_mo_convert_tf.py

@@ -359,7 +359,6 @@ class TestMoConvertTF(CommonMOConvertTest):
     @pytest.mark.nightly
     @pytest.mark.precommit_tf_fe
     @pytest.mark.precommit
-    @pytest.mark.xfail(reason="99426")
     def test_mo_import_from_memory_tf_fe(self, create_model, ie_device, precision, ir_version,
                                          temp_dir):
         fw_model, graph_ref, mo_params = create_model(temp_dir)

tools/mo/openvino/tools/mo/convert_impl.py

@@ -183,7 +183,6 @@ def arguments_post_parsing(argv: argparse.Namespace):
     elif (is_kaldi or is_onnx) and not argv.input_model:
         raise Error('Path to input model is required: use --input_model.')
 
-    log.debug(str(argv))
     log.debug("Model Optimizer started")
 
     log.debug('Output model name would be {}{{.xml, .bin}}'.format(argv.model_name))
@@ -771,6 +770,9 @@ def parse_input_shapes(argv):
 def driver(argv: argparse.Namespace, non_default_params: dict):
     init_logger(argv.log_level.upper(), argv.silent)
 
+    # Log dictionary with non-default cli parameters where complex classes are excluded.
+    log.debug(str(non_default_params))
+
     start_time = datetime.datetime.now()
 
     graph, ngraph_function = prepare_ir(argv)

tools/mo/openvino/tools/mo/front/tf/loader.py

@@ -328,7 +328,8 @@ def convert_to_pb(argv: argparse.Namespace):
     # if this is already binary frozen format .pb, there is no need to create auxiliary binary frozen protobuf
     # the main thing is to differentiate this format from text frozen format and checkpoint
     # that can utilize input_model option
-    if argv.input_model and not argv.input_model_is_text and not argv.input_checkpoint:
+    if argv.input_model and not argv.input_model_is_text and not argv.input_checkpoint and \
+            isinstance(argv.input_model, str):
         return None
 
     user_output_node_names_list = argv.output.split(',') if argv.output else None