// simple like "cast" and keep tracing.
    return traceUpwardsToArgument(v.getDefiningOp()->getOperand(0), seen, out);
  } else {
    // Typically a tf.VarHandle op.
    return v.getDefiningOp()->emitOpError("Non constant predecessor");
  }
}

// For the "resource" attribute in a ReadVariable or AssignVariable op,

  }
};

// Checks if the input producer op is supported in this transform. Right now, we
// only check if it is a tf.IteratorGetNext where resource input is coming from
// a VarHandle on CPU or a function argument assigned to CPU.
bool IsSupportedInputOp(
    Operation* op,
    const TF::ResourceAliasAnalysis::Info& resource_alias_analysis) {

    because the tensors are loaded asynchronously.

  }];

  let arguments = (ins
    TFTensorType:$prefix,
    TFTensorType:$tensor_names,
    TFTensorType:$shape_and_slices,
    Variadic<TFTensorType>:$var_handles,
    TypeArrayAttr: $restored_dtypes,
    DenseBoolArrayAttr: $truncate_in_cast
  );
}

    if (variables.count(GetResourceKey(var_handle_op)))
      work_list.push_back(var_handle_op);
  }

  // Capture list of ops to be erased by traversing usage starting from
  // the VarHandle ops.
  llvm::SetVector<Operation*> erase_list;
  while (!work_list.empty()) {
    auto* operation = work_list.pop_back_val();
    erase_list.insert(operation);
    for (auto& use : operation->getUses()) {

    "IMAG",
    "REAL",
    "COMPLEX_ABS",
    "HASHTABLE",
    "HASHTABLE_FIND",
    "HASHTABLE_IMPORT",
    "HASHTABLE_SIZE",
    "REDUCE_ALL",
    "CONV_3D_TRANSPOSE",
    "VAR_HANDLE",
    "READ_VARIABLE",
    "ASSIGN_VARIABLE",
    "BROADCAST_ARGS",
    "RANDOM_STANDARD_NORMAL",
    "BUCKETIZE",
    "RANDOM_UNIFORM",
    "MULTINOMIAL",
    "GELU",