// Executes a ReadVariableOp using `ctx`. This reads the underlying variable
// value of `variable_handle` and copies the value to `output`. `dtype` must be
// the dtype of the variable associated with `variable_handle`.
Status ReadVariable(ImmediateExecutionContext* ctx,
                    ImmediateExecutionTensorHandle* variable_handle,
                    DataType dtype, ImmediateTensorHandlePtr* output);

    TFL_TensorOf<[F32, F64, I1, UI8, I8, QI8, QUI8, I32, I64, QI16, Complex<F<32>>, Complex<F<64>>]>:$value
  );

  let results = (outs);
}

def TFL_ReadVariableOp : TFL_Op<"read_variable", []> {
  let summary = "Reads variable value.";

  let description = [{
Read variable data identified by 'resource_id'.
  }];

  let arguments = (ins
    TFL_ResourceTensor:$resource_id

  TF_RETURN_IF_ERROR(assign_op->AddInput(value));

  int num_retvals = 0;
  TF_RETURN_IF_ERROR(assign_op->Execute({}, &num_retvals));
  return Status();
}

Status ReadVariable(ImmediateExecutionContext* ctx,
                    ImmediateExecutionTensorHandle* variable_handle,
                    DataType dtype, ImmediateTensorHandlePtr* output) {
  ImmediateOpPtr read_op(ctx->CreateOperation());

  [(CopyAttrs $src, $dest1), (CopyAttrs $src, $dest2)]>;


//===----------------------------------------------------------------------===//
// Cast op followed by a ReadVariable op can be folded into the ReadVariable
//===----------------------------------------------------------------------===//

def ReadVariableOfCast : Pat<
  (TF_ReadVariableOp:$src (TF_CastOp:$output $x, BoolAttr:$Truncate)),

// RUN: tf-opt %s -tf-tpu-colocate-composite-resource-ops | FileCheck %s

// Tests ReadVariable op using composite device resource is wrapped inside
// tf_device.Cluster.

// CHECK-LABEL: func @testReadVariableOpColocated
// CHECK-SAME: (%[[ARG0:.*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)
func.func @testReadVariableOpColocated(%arg0: tensor<*x!tf_type.resource<tensor<4xf32>>>) {
  // CHECK:      tf_device.replicate

  bool shape_inference = true;
  // Whether to do TFLite runtime verification.
  bool runtime_verification = true;
  // Whether to enable TFLite variables or not, this will allow
  // mutable variables and produce ReadVariable/AssignVariable ops in TFLite.
  bool enable_tflite_variables = false;
  // Whether to disable the variable freezing pass or not.
  // By default we freeze all variables and disallow mutable variables. When

  } else {
    // Typically a tf.VarHandle op.
    return v.getDefiningOp()->emitOpError("Non constant predecessor");
  }
}

// For the "resource" attribute in a ReadVariable or AssignVariable op,
// determine the symbol reference to the (new) ml_program::GlobalOp.
SymbolRefAttr lookupGlobalTensor(func::FuncOp func, Value resource,
                                 SymbolTable &syms,

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteExecutor(executor);
  TF_DeleteStatus(status);
}

// Read the value of variable `var` and save it into `out_value`.
void ReadVariable(TFE_Context* ctx, TFE_TensorHandle* var,
                  TFE_TensorHandle** out_value) {
  TF_Status* status = TF_NewStatus();
  TFE_Op* op = TFE_NewOp(ctx, "ReadVariableOp", status);

}

// Returns a TPUPartitionedInput op connected to a `tf_device.cluster_func`
// operand value if it has an XLA sharding. If value is a resource type then
// TPUPartitionedInput op will be connected to a ReadVariable op that feeds into
// a `tf_device.cluster_func`.
mlir::Operation* GetXlaShardingFromOperand(Value value) {
  Value value_to_visit = value;
  if (auto read_var = value_to_visit.getDefiningOp<mlir::TF::ReadVariableOp>())

      }
    }
  }
  for (auto cluster_operand : cluster.getOperands()) {
    Operation* def = cluster_operand.getDefiningOp();
    // This pass assumes that a TPUPartitionedInputV2 is preceeded by
    // ReadVariable ops, and not vice versa. An earlier pass,
    // TPUResourceReadsWritesPartitioning, should have ensured this
    // precondition.
    if (!def) continue;
    for (auto operand : def->getOperands()) {