Documentation of parameter vm_size for AMLcompute is not right

Values of vm_size in Azure ML SDK [documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class)?view=azure-ml-py#provisioning-configuration-vm-size-----vm-priority--dedicated---min-nodes-0--max-nodes-none--idle-seconds-before-scaledown-none--admin-username-none--admin-user-password-none--admin-user-ssh-key-none--vnet-resourcegroup-name-none--vnet-name-none--subnet-name-none--tags-none--description-none--remote-login-port-public-access--notspecified---identity-type-none--identity-id-none-) is linked to [here](https://docs.microsoft.com/en-us/azure/templates/Microsoft.Compute/virtualMachines?toc=%2Fen-us%2Fazure%2Fazure-resource-manager%2Ftoc.json&bc=%2Fen-us%2Fazure%2Fbread%2Ftoc.json#hardwareprofile-object). If Azure ML SDK documentation.

But supported vm_size are somethign different. See the error message.

`Provisioning errors: [{'error': {'code': 'InvalidPropertyValue', 'message': 'The specified value Standard_B2s for property Cluster.Properties.VMSize is not a supported VM size. See additional details for supported VM sizes.', 'details': [{'code': 'SupportedVMSizes', 'message': 'STANDARD_D1,STANDARD_D2,STANDARD_D3,STANDARD_D4,STANDARD_D11,STANDARD_D12,STANDARD_D13,STANDARD_D14,STANDARD_D1_V2,STANDARD_D2_V2,STANDARD_D3_V2,STANDARD_D4_V2,STANDARD_D11_V2,STANDARD_D12_V2,STANDARD_D13_V2,STANDARD_D14_V2,STANDARD_D15_V2,STANDARD_D2_V3,STANDARD_D4_V3,STANDARD_D8_V3,STANDARD_D16_V3,STANDARD_D32_V3,STANDARD_D64_V3,STANDARD_DS1_V2,STANDARD_DS2_V2,STANDARD_DS3_V2,STANDARD_DS4_V2,STANDARD_DS5_V2,STANDARD_DS11_V2,STANDARD_DS12_V2,STANDARD_DS13_V2,STANDARD_DS14_V2,STANDARD_DS15_V2,STANDARD_D2S_V3,STANDARD_D4S_V3,STANDARD_D8S_V3,STANDARD_D16S_V3,STANDARD_D32S_V3,STANDARD_D64S_V3,STANDARD_F2S_V2,STANDARD_F4S_V2,STANDARD_F8S_V2,STANDARD_F16S_V2,STANDARD_F32S_V2,STANDARD_F64S_V2,STANDARD_F72S_V2,STANDARD_NC6,STANDARD_NC12,STANDARD_NC24,STANDARD_NC24r,STANDARD_NC6S_V2,STANDARD_NC12S_V2,STANDARD_NC24S_V2,STANDARD_NC24RS_V2,STANDARD_NC6S_V3,STANDARD_NC12S_V3,STANDARD_NC24S_V3,STANDARD_NC24RS_V3,STANDARD_ND6S,STANDARD_ND12S,STANDARD_ND24S,STANDARD_ND24RS,STANDARD_ND40s_V2,STANDARD_ND40rs_V2,STANDARD_NV6,STANDARD_NV12,STANDARD_NV24,Standard_M8-4ms,Standard_M8ms,Standard_M16-4ms,Standard_M16-8ms,Standard_M16ms,Standard_M32-8ms,Standard_M32-16ms,Standard_M32ls,Standard_M32ms,Standard_M32ts,Standard_M64-16ms,Standard_M64-32ms,Standard_M64ls,Standard_M64ms,Standard_M64s,Standard_M128-32ms,Standard_M128-64ms,Standard_M128ms,Standard_M128s,Standard_M64,Standard_M64m,Standard_M128,Standard_M128m,Standard_M8-2ms,Standard_NV12s_v3,Standard_NV24s_v3,Standard_NV48s_v3,Standard_HB120rs_v2,Standard_HC44rs'}]}}]`

Another issue with this situation is that the provision state is "Failed" (see logs`AmlCompute(workspace=Workspace.create(name='xxxws', subscription_id='dddddddd', resource_group='rg'), name=amlcompute, id=id, type=AmlCompute, provisioning_state=Failed, location=xxxx, .........` ) and when try to fetch provision_state with `ComputeTarget.get_status()` function in the next execution/run, it returns `None` instead of string of 'Failed'. It is not possible to delete programmatically and it must be delete manually.