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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def add_embedding(self, name, W, b, input_dim, output_channels, has_bias, input_name, output_name):
 """ Add an embedding layer to the model. Parameters name: str The name of this layer W: numpy.array Weight matrix of shape (output_channels, input_dim). b: numpy.array Bias vector of shape (output_channels, ). input_dim: int Size of the vocabulary (1 + maximum integer index of the words). output_channels: int Number of output channels. has_bias: boolean Whether the bias vector of this layer is ignored in the spec. - If True, the bias vector of this layer is not ignored. - If False, the bias vector is ignored. input_name: str The input blob name of this layer. output_name: str The output blob name of this layer. See Also -------- add_inner_product """


        spec = self.spec
        nn_spec = self.nn_spec

        # Add a new layer
        spec_layer = nn_spec.layers.add()
        spec_layer.name = name
        spec_layer.input.append(input_name)
        spec_layer.output.append(output_name)

        # Fill in the parameters
        spec_layer_params = spec_layer.embedding

        spec_layer_params.inputDim = input_dim
        spec_layer_params.outputChannels = output_channels
        spec_layer_params.hasBias = has_bias

        weights = spec_layer_params.weights
        weights.floatValue.extend(map(float, W.flatten()))
        if has_bias:
            bias = spec_layer_params.bias
            bias.floatValue.extend(map(float, b.flatten()))