AzureML CLI v2 - R api - Mlflow causing pipeline to break

- If the issue is to do with Azure CLI 2.0 in-particular, create an issue here at [Azure/azure-cli](https://github.com/Azure/azure-cli/issues)

### Related command
az ml job create

### Extension name (the extension in question)
ml

### Description of issue (in as much detail as possible)
When running a ml component running R api mlflow code block using "with statement" block, following documentation example (https://mlflow.org/docs/latest/R-api.html#mlflow-start-run)

The component does not pass output to input of following step correctly, The input is passed as a "DataReference" and does not point to correct location. When removing the "with statement" block the pipeline runs correctly.

ComponentA working:

component.r:
library(optparse)
library("carrier")
library(mlflow)

options <- list(
make_option(c("-d", "--data_folder"), default="./data"),
make_option(c("-o", "--out_folder"), default="./out")
)

opt_parser <- OptionParser(option_list = options)
opt <- parse_args(opt_parser)

paste(opt$data_folder)
paste(opt$out_folder)

run <- mlflow_start_run()
accidents <- readRDS(file.path(opt$data_folder, "accidents.Rd"))
summary(accidents)

saveRDS(accidents, file.path(opt$out_folder, "predictions.Rd"))

--------------------------------------
Pipeline component json, subsequent step:
"runDefinition": {
"script": null,
"command": "Rscript eval_model.r --model $AZUREML_DATAREFERENCE_component_b_input",
"useAbsolutePath": false,
"arguments": [],
"sourceDirectoryDataStore": null,
"framework": "Python",
"communicator": "None",
"target": "cpu-cluster",
"dataReferences": {},
"data": {},
"inputAssets": {
"component_b_input": {
"asset": {
"assetId": "azureml://locations/westus2/workspaces/dc720ed6-90b3-49cb-8383-0897f5db4402/data/azureml_3450fa88-128c-4026-9065-a0e214261911_output_data_component_a_output/versions/1",
"type": "UriFolder"
},
"mechanism": "Mount",
"environmentVariableName": "AZURE_ML_INPUT_component_b_input",
"pathOnCompute": null,
"overwrite": true,
"options": {
"IsEvalMode": "False",
"ReadWrite": "False",
"ForceFolder": "False"
}
}
},

ComponentA - results in error:
component.r:

library(optparse)
library("carrier")
library(mlflow)

options <- list(
make_option(c("-d", "--data_folder"), default="./data"),
make_option(c("-o", "--out_folder"), default="./out")
)

opt_parser <- OptionParser(option_list = options)
opt <- parse_args(opt_parser)

paste(opt$data_folder)
paste(opt$out_folder)

with(run <- mlflow_start_run(), {
accidents <- readRDS(file.path(opt$data_folder, "accidents.Rd"))
summary(accidents)

saveRDS(accidents, file.path(opt$out_folder, "predictions.Rd"))
})

------------------------------------------------
Pipeline component json, subsequent step:

"runDefinition": {
"script": null,
"command": "Rscript eval_model.r --model $AZUREML_DATAREFERENCE_model",
"useAbsolutePath": false,
"arguments": [],
"sourceDirectoryDataStore": null,
"framework": "Python",
"communicator": "None",
"target": "cpu-cluster",
"dataReferences": {
"model": {
"dataStoreName": "workspaceblobstore",
"mode": "Mount",
"pathOnDataStore": "azureml/{name}/model_out/",
"pathOnCompute": null,
"overwrite": true
}
},
"inputs" : null
-------
steps to reproduce:
- open attached
- create environment from docker file
- uncomment lines /src/accident.R ln:27 and ln:46
- run az ml create job -f pipeline.yml

[accidents-copy.zip](https://github.com/Azure/azure-cli-extensions/files/9329225/accidents-copy.zip)
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