%2 = "tf.MatMul"(%arg0, %1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  // CHECK-NEXT: [[r1:%.*]] = tfrt_fallback_async.executeop {{.*}} "tf.BiasAdd"([[r0]], [[result]])

tuple_value: {
    values: {
    tuple_value: {
        values: {
        tensor_spec_value: {
            name : "x"
            shape: {
            dim: {
                size: 1
            }
            dim: {
                size: 10
            }
            }
            dtype: DT_FLOAT
        }
        }
    }
    }
    values: {
    dict_value: {
    }
    }

    key: "dtype"
    value {
      type: DT_DOUBLE
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_DOUBLE
        tensor_shape {
          dim {
            size: 2
          }
          dim {
            size: 3
          }
        }

				return
			}
		}
	}

	// Something went wrong. Just pass the line through.
	f.w.Write(line)
}

type jsonValue struct {
	atom json.Token  // If json.Delim, then seq will be filled
	seq  []jsonValue // If atom == json.Delim('{'), alternating pairs
}

var jsonPop = errors.New("end of JSON sequence")

func decodeJSONValue(dec *json.Decoder) (jsonValue, error) {
	t, err := dec.Token()

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "x_hold" }
  shape {
    dim { size: <M> }
    dim { size: <K> }
  }
}
feed {
  id { node_name: "y_hold" }
  shape {
    dim { size: <K> }
    dim { size: <N> }
  }
}
fetch {
  id { node_name: "x_y_prod" }

  // CHECK: device = "gpu"
  %1 = "tf.MatMul"(%arg0, %0) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "cpu"
  %2 = "tf.Relu"(%1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "cpu"} : (tensor<3x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "gpu"

		fmt.Printf("\n")
	}
	// Output:
	// json.Delim: { (more)
	// string: Message (more)
	// string: Hello (more)
	// string: Array (more)
	// json.Delim: [ (more)
	// float64: 1 (more)
	// float64: 2 (more)
	// float64: 3
	// json.Delim: ] (more)
	// string: Null (more)
	// <nil>: <nil> (more)
	// string: Number (more)
	// float64: 1.234
	// json.Delim: }
}

  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 1
        }
        dim {
          size: 8
        }
        dim {
          size: 8
        }
        dim {
          size: 2
        }
      }
    }
  }
}
node {
  name: "conv_net_2d/conv_2d_0/w"
  op: "Const"
  attr {

      }
    }
  }
  attr {
    key: "_handle_shapes"
    value {
      list {
        shape {
          dim {
            size: 3
          }
          dim {
            size: 3
          }
          dim {
            size: 1
          }
          dim {
            size: 32
          }
        }
      }
    }
  }
  attr {
    key: "index"
    value {
      i: 1

# Text form of tensorflow.tf2xla.Config proto.
feed {
  id { node_name: "x_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "y_const" }
  shape {
    dim { size: 1 }
  }
}
feed {
  id { node_name: "z" output_index: 1}
  shape {
    dim { size: 1 }
  }
  type: DT_INT32
}
fetch {
  id { node_name: "x_y_sum" }
}
fetch {
  id { node_name: "x_z_sum" }