update table format

examples/tutorials/run-flow-with-pipeline/pipeline.ipynb

@@ -133,9 +133,9 @@
     "When using the `load_component` function and the flow YAML specification, your flow is automatically transformed into a __[parallel component](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-job-in-pipeline?view=azureml-api-2&tabs=cliv2#why-are-parallel-jobs-needed)__. This parallel component is designed for large-scale, offline, parallelized processing with efficiency and resilience. Here are some key features of this auto-converted component:\n",
     "\n",
     " - Pre-defined input and output ports:\n",
-    " \n",
-    "   | port name  |  type  | description |\n",
-    "   | ---------- | ------ | ----------- |\n",
+    "\n",
+    "   | port name | type | description |\n",
+    "   | --------- | ---- | ----------- |\n",
     "   | data | uri_folder or uri_file | Accepts batch data input to your flow. You can use either the `uri_file` data type if your data is a single file or the `uri_folder` data type if your folder contains multiple files with the same schema. The default data type is jsonl, but you can customize this setting after declaring an instance of this flow component in your pipeline. Note that your data will be converted into a dataframe, so ensure that your CSV or TSV data includes a header line for proper mapping. |\n",
     "   | flow_outputs | uri_file | Generates a single output file named parallel_run_step.jsonl. Each line in this data file corresponds to a JSON object representing the flow returns, along with an additional column called line_number indicating its position from the original file. |\n",
     "   | debug_info | uri_folder | If you run your flow component in __debug mode__, this port provides debugging information for each run of your lines. E.g. intermediate outputs between steps, or LLM response and token usage. |\n",