Hello, this is my first time posting here. Let me know whether this question is suited to be posted to this forum or not.
I have been trying to translate tensorflow models into MLIR-HLO. As such I successfully used tf-mlir-translate , tf-opt with very simple TF models like Conv2D, MatMul,MaxPool…, achieving the expected results.
However, if I try to do the same with the official example tf.Variable  with the three steps below:

tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false target.pbtxt -tf-prune-unused-nodes -tf-control-output-arrays=Variable/Assign,AssignAdd -o target.mlir
tf-opt -tf-executor-to-functional-conversion target.mlir -o target-func.mlir
tf-opt --tf-to-hlo-pipeline target-func.mlir -o target-mhlo.mlir

I get the following error message with the 3’rd step:
target-func.mlir:2:3: error: The following operations cannot be legalized: tf.Assign (count: 1); tf.AssignAdd (count: 1); tf.VariableV2 (count: 1). These legalization failure(s) may be due to missing TF to HLO lowerings and/or unsupported attributes, etc.
  func @main() attributes {tf.entry_function = {control_outputs = "Variable/Assign,AssignAdd", inputs = "", outputs = ""}} {
  ^
target-func.mlir:2:3: error: Emitting more detail about one op that failed to legalize...
  func @main() attributes {tf.entry_function = {control_outputs = "Variable/Assign,AssignAdd", inputs = "", outputs = ""}} {
  ^
target-func.mlir:7:10: error: 'tf.Assign' op is not legalizable
    %2 = "tf.Assign"(%0, %cst_0) {_class = ["loc:@Variable"], device = "", use_locking = true, validate_shape = true} : (tensor<!tf.f32ref>, tensor<f32>) -> tensor<*x!tf.f32ref>
         ^
target-func.mlir:7:10: note: see current operation: %4 = "tf.Assign"(%2, %0) {_class = ["loc:@Variable"], device = "", use_locking = true, validate_shape = true} : (tensor<!tf.f32ref>, tensor<f32>) -> tensor<!tf.f32ref>


As of right now, is there a way to lower these tf operations into mhlo-hlo ?