def METHOD_NAME(self, input_shape, **kwargs):
    """Construct a compiled, un-trained, keras model that is ready for training.
    In sktime, time series are stored in numpy arrays of shape (d,m), where d
    is the number of dimensions, m is the series length. Keras/tensorflow assume
    data is in shape (m,d). This method also assumes (m,d). Transpose should
    happen in fit.
    Parameters
    ----------
    input_shape : tuple
        The shape of the data fed into the input layer, should be (m,d)
    Returns
    -------
    output : a compiled Keras Model
    """
    import tensorflow as tf
    from tensorflow import keras
    tf.random.set_seed(self.random_state)
    if self.metrics is None:
        metrics = ["accuracy"]
    else:
        metrics = self.metrics
    input_layer, output_layer = self._network.build_network(input_shape, **kwargs)
    output_layer = keras.layers.Dense(
        units=1,
        activation=self.activation,
        use_bias=self.use_bias,
    )(output_layer)
    self.optimizer_ = (
        keras.optimizers.Adam(learning_rate=0.01)
        if self.optimizer is None
        else self.optimizer
    )
    model = keras.models.Model(inputs=input_layer, outputs=output_layer)
    model.compile(
        loss=self.loss,
        optimizer=self.optimizer_,
        metrics=metrics,
    )
    return model