Does Machine Learning Codes can be access from Studio UI?.

I have uploaded the batch_driver.py file in a storage account, then I have created this Code using Bicep template:

param codeAssetDescription string = 'This is a test description for a code asset created by an ARM template'

@description('If the name version are system generated (anonymous registration).')
param isAnonymous bool = false

param workspaceName string = 'gmatheus01rmlw'

param codeAssetName string = 'codeTestGui'

resource workspaceName_codeAssetName_codeAssetVersion 'Microsoft.MachineLearningServices/workspaces/codes/versions@2022-05-01' = {
  name: '${workspaceName}/${codeAssetName}/1'
  properties: {
    description: codeAssetDescription
    #disable-next-line no-hardcoded-env-urls
    codeUri: 'https://storageAccount.blob.core.windows.net/azureml-blobstore-005340c3-177e-469c-98c1-dd71d2c77a11/models/hr/scoring/'
    isArchived: false
    isAnonymous: isAnonymous
    properties: {}
    tags: {}
  }
}

output codeVersionId string = workspaceName_codeAssetName_codeAssetVersion.id
output codeVersionName string = workspaceName_codeAssetName_codeAssetVersion.name
output codeVersionUri string = workspaceName_codeAssetName_codeAssetVersion.properties.codeUri

Which is then, defined in codeId in batch template below. So I am creating a Batch Endpoint that will rely on a scoring script as it's mentioned here in Author scoring scripts for batch deployments, we can also find examples in here Deploy models for scoring in batch endpoints. So I have created this Batch Endpoint template using Bicep:

metadata description = 'Deploys a Batch Endpoint to an existing Azure Machine Learning workspace.'

@description('Specifies the name of the Azure Machine Learning workspace.')
param workspaceName string

@description('Specifies the location for all resources.')
param location string

@description('Specifies the batch endpoints and deployments to be created.')
param endpoints array

param ARMId string

@description('Tags to be applied to this resource.')
param tags object = {
  MLOps: true
}

@description('References the existing Azure Machine Learning workspace resource.')
resource mlWorkspace 'Microsoft.MachineLearningServices/workspaces@2024-10-01' existing = {
  name: workspaceName
}

@description('Flatten the deployments array from the endpoints parameter.')
var deployments = flatten(map(
  endpoints,
  (endpoint, i) => map(endpoint.deployments, deployment => union({ endpointIndex: i }, deployment))
))

@description('References the existing Azure Machine Learning environments.')
resource mlEnvironments 'Microsoft.MachineLearningServices/workspaces/environments@2024-10-01' existing = [
  for (deployment, i) in deployments: {
    name: deployment.environmentName
    parent: mlWorkspace
  }
]

@description('References the existing Azure Machine Learning environment versions.')
resource mlEnvironmentsVersions 'Microsoft.MachineLearningServices/workspaces/environments/versions@2024-10-01' existing = [
  for (deployment, i) in deployments: {
    name: mlEnvironments[i].properties.latestVersion
    parent: mlEnvironments[i]
  }
]

@description('References the existing Azure Machine Learning models in the workspace.')
resource mlModels 'Microsoft.MachineLearningServices/workspaces/models@2025-07-01-preview' existing = [
  for (deployment, i) in deployments: {
    name: deployment.modelName
    parent: mlWorkspace
  }
]

@description('References the existing versions of the Azure Machine Learning models in the workspace.')
resource mlModelsVersion 'Microsoft.MachineLearningServices/workspaces/models/versions@2025-07-01-preview' existing = [
  for (deployment, i) in deployments: {
    name: mlModels[i].properties.latestVersion
    parent: mlModels[i]
  }
]

@description('Creates a batch endpoint for model inference in the Azure Machine Learning workspace.')
resource batchEndpoint 'Microsoft.MachineLearningServices/workspaces/batchEndpoints@2025-06-01' = [
  for (endpoint, i) in endpoints: if (length(endpoints) > 0) {
    name: endpoint.name
    parent: mlWorkspace
    location: location
    identity: {
      type: 'SystemAssigned'
    }
    properties: {
      authMode: 'AADToken'
      description: endpoint.description
    }
    tags: tags
  }
]

@description('The Azure Machine Learning compute cluster resource for the workspace.')
resource computes 'Microsoft.MachineLearningServices/workspaces/computes@2025-06-01' existing = [
  for (deployment, i) in deployments: {
    name: deployment.computeName
    parent: mlWorkspace
  }
]

@description('Creates model deployments for the specified endpoints in the Azure Machine Learning workspace.')
resource batchDeployment 'Microsoft.MachineLearningServices/workspaces/batchEndpoints/deployments@2025-06-01' = [
  for (deployment, i) in deployments: if (length(deployments) > 0) {
    name: deployment.name
    parent: batchEndpoint[deployment.endpointIndex]
    location: location
    properties: {
      description: deployment.description
      compute: computes[i].id
      errorThreshold: -1
      loggingLevel: 'Debug'
      maxConcurrencyPerInstance: 1
      miniBatchSize: 10
      environmentId: mlEnvironmentsVersions[i].id
      model: {
        referenceType: 'Id'
        assetId: mlModelsVersion[i].id
      }
      codeConfiguration: {
        codeId: 'ARMId'
        scoringScript: 'batch_driver.py'
      }
      outputAction: 'AppendRow'
      outputFileName: 'predictions.csv'
      retrySettings: {
        maxRetries: 3
      }
    }
    tags: tags
  }
]

@description('Output endpoints created in the Azure Machine Learning workspace.')
output batchEndpoints array = [
  for i in range(0, length(endpoints)): {
    batchEndpointName: batchEndpoint[i].name
    batchEndpointId: batchEndpoint[i].id
    batchDeploymentName: batchDeployment[i].name
    batchDeploymentId: batchDeployment[i].id
  }
]

@description('Output model information from the Azure Machine Learning workspace.')
output modelIds array = [
  for i in range(0, length(endpoints)): {
    modelId: mlModels[i].name
    modelVersion: mlModelsVersion[i].id
  }
]

And my deployment also succeeds:

But the real question is: I can see the batch endpoint created in my workspace, but where can I see that Code that was created? Is it something that can be seen through Studio?