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README.md

@@ -135,14 +135,11 @@ from qai_hub_models.models.mediapipe_pose import MediaPipePoseDetector,MediaPipe
 
 # Load the model
 pose_detector_model = MediaPipePoseDetector.from_pretrained()
-
 pose_landmark_detector_model = MediaPipePoseLandmarkDetector.from_pretrained()
 
-
 # Device
 device = hub.Device("Samsung Galaxy S23")
 
-
 # Trace model
 pose_detector_input_shape = pose_detector_model.get_input_spec()
 pose_detector_sample_inputs = pose_detector_model.sample_inputs()
@@ -158,7 +155,6 @@ pose_detector_compile_job = hub.submit_compile_job(
 
 # Get target model to run on-device
 pose_detector_target_model = pose_detector_compile_job.get_target_model()
-
 # Trace model
 pose_landmark_detector_input_shape = pose_landmark_detector_model.get_input_spec()
 pose_landmark_detector_sample_inputs = pose_landmark_detector_model.sample_inputs()
@@ -185,12 +181,10 @@ After compiling models from step 1. Models can be profiled model on-device using
 provisioned in the cloud.  Once the job is submitted, you can navigate to a
 provided job URL to view a variety of on-device performance metrics.
 ```python
-
 pose_detector_profile_job = hub.submit_profile_job(
         model=pose_detector_target_model,
         device=device,
         )       
-
 pose_landmark_detector_profile_job = hub.submit_profile_job(
         model=pose_landmark_detector_target_model,
         device=device,