```mlir
    tf_device.replicate([%0, %1] as %ri: tensor<*x!tf_type.resource>) {n = 2 : i32} {
      %2 = "tf.ReadVariableOp"(%ri) : tensor<*x!tf_type.resource> -> tensor<*xi32>
      %3 = "tf.VariableShape"(%0) : (tensor<*x!tf_type.resource>) -> tensor<?xi32>
      tf_device.return
    }
    ```
  }];

  let dependentDialects = [
    "mhlo::MhloDialect",
    "tf_device::TensorFlowDeviceDialect"

  let results = (outs
    TF_Tensor:$ref
  );

  TF_DerivedResultTypeAttr dtype = TF_DerivedResultTypeAttr<0>;

  let hasCanonicalizer = 1;
}

def TF_VariableShapeOp : TF_Op<"VariableShape", []> {
  let summary = "Returns the shape of the variable pointed to by `resource`.";

  let description = [{
This operation returns a 1-D integer tensor representing the shape of `input`.