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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 receptive_field(self, X, identities, max_len=10, threshold=0.9, batch_size=1):
 """ Calculate the receptive field of the SOM on some data. The receptive field is the common ending of all sequences which lead to the activation of a given BMU. If a SOM is well-tuned to specific sequences, it will have longer receptive fields, and therefore gives a better description of the dynamics of a given system. Parameters X : numpy array Input data. identities : list A list of symbolic identities associated with each input. We expect this list to be as long as the input data. max_len : int, optional, default 10 The maximum length to attempt to find. Raising this increases memory use. threshold : float, optional, default .9 The threshold at which we consider a receptive field valid. If at least this proportion of the sequences of a neuron have the same suffix, that suffix is counted as acquired by the SOM. batch_size : int, optional, default 1 The batch size to use in prediction Returns ------- receptive_fields : dict A dictionary mapping from the neuron id to the found sequences for that neuron. The sequences are represented as lists of symbols from identities. """

        receptive_fields = defaultdict(list)
        predictions = self.predict(X, batch_size)

        if len(predictions) != len(identities):
            raise ValueError("X and identities are not the same length: "
                             "{0} and {1}".format(len(X), len(identities)))

        for idx, p in enumerate(predictions.tolist()):
            receptive_fields[p].append(identities[idx+1 - max_len:idx+1])

        rec = {}

        for k, v in receptive_fields.items():
            # if there's only one sequence, we don't know
            # anything abouw how salient it is.
            seq = []
            if len(v) <= 1:
                continue
            else:
                for x in reversed(list(zip(*v))):
                    x = Counter(x)
                    if x.most_common(1)[0][1] / sum(x.values()) > threshold:
                        seq.append(x.most_common(1)[0][0])
                    else:
                        rec[k] = seq
                        break

        return rec