How to use the yarl.components.Component function in yarl

# limitations under the License.
# ==============================================================================

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.contrib import autograph
import tensorflow as tf
import numpy as np

from yarl.components import Component
from yarl.spaces import Continuous, Tuple, Dict


class Memory(Component):
    def __init__(self, *args, **kwargs):
        super(Memory, self).__init__(*args, **kwargs)
        self.record_space = Dict(
            state=Dict(state1=float, state2=float),
            reward=float
        )
        self.memory = self.get_variable("memory", trainable=False, from_space=self.record_space)
        self.index = self.get_variable("index", trainable=False, dtype=int)
        self.capacity = 5
        # TODO: put this into splitter component:
        self.var_list = [self.memory["state"]["state1"], self.memory["state"]["state2"], self.memory["reward"]]

        # TODO: this is normally done automatically by Component (see commented-out code below)
        self.insert = autograph.to_graph(self.computation_insert, verbose=True, partial_types={int})  #, partial_types={str, tuple})
        #self.define_inputs("record")
        #self.define_outputs("insert")

# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

from yarl.components import Component


class GaussianNoise(Component):
    pass

from __future__ import print_function

from math import log
import numpy as np
from six.moves import xrange as range_

from yarl import get_backend, YARLError, SMALL_NUMBER
from yarl.components import Component
from yarl.components.layers import DenseLayer
from yarl.spaces import Space, IntBox, ContainerSpace

if get_backend() == "tf":
    import tensorflow as tf


class NNOutputAdapter(Component):
    """
    A Component that cleans up a neural network's output and gets it ready for parameterizing a Distribution Component.
    Cleanup includes reshaping (for the desired action space), adding a distribution bias, making sure probs are not
    0.0 or 1.0, etc..

    API:
    ins:
        nn_output (SingleDataOp): The raw neural net output to be cleaned up for further processing in a
            Distribution.
    outs:
        logits (SingleDataOp): The reshaped, cleaned-up NN output logits (after possible bias) but before being
            translated into Distribution parameters (e.g. via Softmax).
        parameters (SingleDataOp): The final results of translating the raw NN-output into Distribution-readable
            parameters.
    """
    def __init__(self, action_output_name=None, biases_spec=False, scope="nn-output-adapter", **kwargs):

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from yarl import backend
from yarl.components import Component

if backend == 'tf':
    import tensorflow as tf


class NoiseComponent(Component):
    """
    A base class Component that takes an action input and outputs some noise value.

    API:
    ins:
        action (float): The action value input.
    outs:
        noise (float): The noise value to be added to the action.
    """
    def __init__(self, action_space, scope="noise", **kwargs):
        """

        Args:
            action_space: The action space.
        """
        super(NoiseComponent, self).__init__(scope=scope, **kwargs)