// attributes.
  for (const tensorflow::OpDef::ArgDef& output_arg : op_def_->output_arg()) {
    int original_size = state_->types.size();
    if (!output_arg.number_attr().empty()) {
      // Same type repeated "repeats" times.
      Attribute repeats_attr = attrs_[output_arg.number_attr()];
      if (!repeats_attr)
        return InvalidArgument("Missing attribute '", output_arg.number_attr(),

void OpImplementationRenderer::RenderExecutionListOp() {
  ArgView output_arg = op_.OnlyOutput();
  Statement("int num_retvals = $0.size()", output_arg.VariableName());
  Statement("return " + Call(op_.VariableName(), "Execute",
                             {output_arg.VariableName(), "&num_retvals"}));
}

void OpImplementationRenderer::RenderExecutionSingleOutput() {
  ArgView output_arg = op_.OnlyOutput();
  Statement("int num_retvals = 1");

        name: "cond_true"
        type: DT_INT32
      }
      input_arg {
        name: "cond_true1"
        type: DT_INT32
      }
      output_arg {
        name: "cond_true2"
        type: DT_INT32
      }
      output_arg {
        name: "cond_true3"
        type: DT_INT32
      }
    }
    node_def {
      name: "Identity"
      op: "Identity"
      input: "cond_true"

        type: DT_INT32
      }
      input_arg {
        name: "input"
        type: DT_INT32
      }
      output_arg {
        name: "output"
        type: DT_INT32
      }
      output_arg {
        name: "output_1"
        type: DT_INT32
      }
      output_arg {
        name: "output_2"
        type: DT_INT32
      }
    }
    node_def {
      name: "Add/y"

      }
      input_arg {
        name: "cond_true_arg2"
        type: DT_FLOAT
      }
      input_arg {
        name: "cond_true_arg3"
        type: DT_FLOAT
      }
      output_arg {
        name: "cond_true_ret"
        type: DT_FLOAT
      }
    }
    node_def {
      name: "tf.Add"
      op: "Add"
      input: "cond_true_arg2"
      input: "cond_true_arg3"
      attr {

          name: "dense_defaults"
          type_list_attr: "Tdense"
        }
        output_arg {
          name: "sparse_indices"
          type: DT_INT64
          number_attr: "Nsparse"
        }
        output_arg {
          name: "sparse_values"
          type_list_attr: "sparse_types"
        }
        output_arg {
          name: "sparse_shapes"
          type: DT_INT64
          number_attr: "Nsparse"

  }
  attr {
    key: "output_shapes"
    value {
      list {
      }
    }
  }
  experimental_debug_info {
  }
}
library {
  function {
    signature {
      name: "bar"
      output_arg {
        name: "bar"
        type: DT_FLOAT
      }
    }
    node_def {
      name: "const_2"
      op: "Const"
      attr {
        key: "dtype"
        value {
          type: DT_FLOAT

    "'raw_outputs/class_predictions' type: DT_FLOAT } input_arg: { name: "
    "'anchors' type: DT_FLOAT } output_arg: { name: "
    "'TFLite_Detection_PostProcess' type: DT_FLOAT } output_arg: { name: "
    "'TFLite_Detection_PostProcess:1' type: DT_FLOAT } output_arg: { name: "
    "'TFLite_Detection_PostProcess:2' type: DT_FLOAT } output_arg: { name: "
    "'TFLite_Detection_PostProcess:3' type: DT_FLOAT } attr : { name: "

      input_arg {
        name: "body"
        type: DT_INT32
      }
      input_arg {
        name: "body1"
        type: DT_FLOAT
      }
      output_arg {
        name: "body2"
        type: DT_INT32
      }
      output_arg {
        name: "body3"
        type: DT_FLOAT
      }
    }
    node_def {
      name: "Const"
      op: "Const"
      attr {
        key: "dtype"

    }
  }
}
library {
  function {
    signature {
      name: "indexed_case_branch0_4"
      input_arg {
        name: "add_const"
        type: DT_INT32
      }
      output_arg {
        name: "add"
        type: DT_INT32
      }
    }
    node_def {
      name: "add/y"
      op: "Const"
      attr {
        key: "dtype"
        value {
          type: DT_INT32