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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 from_samples(cls, samples_like, vectors, info, vartype, variable_labels=None):
 """Build a response from samples. Args: samples_like: A collection of samples. 'samples_like' is an extension of NumPy's array_like to include an iterable of sample dictionaries (as returned by :meth:`.Response.samples`). data_vectors (dict[field, :obj:`numpy.array`/list]):
 Additional per-sample data as a dict of vectors. Each vector is the same length as `samples_matrix`. The key 'energy' and it's vector is required. info (dict):
 Information about the response as a whole formatted as a dict. vartype (:class:`.Vartype`/str/set):
 Variable type for the response. Accepted input values: * :class:`.Vartype.SPIN`, ``'SPIN'``, ``{-1, 1}`` * :class:`.Vartype.BINARY`, ``'BINARY'``, ``{0, 1}`` variable_labels (list, optional):
 Determines the variable labels if samples_like is not an iterable of dictionaries. If samples_like is not an iterable of dictionaries and if variable_labels is not provided then index labels are used. Returns: :obj:`.Response` Examples: From dicts From an array """


        # there is no np.is_array_like so we use a try-except block
        try:
            # trying to cast it to int8 rules out list of dictionaries. If we didn't try to cast
            # then it would just create a vector of np.object
            samples = np.asarray(samples_like, dtype=np.int8)
        except TypeError:
            # if labels are None, they are set here
            samples, variable_labels = _samples_dicts_to_array(samples_like, variable_labels)

        assert samples.dtype == np.int8, 'sanity check'

        record = data_struct_array(samples, **vectors)

        # if labels are still None, set them here. We could do this in an else in the try-except
        # block, but the samples-array might not have the correct shape
        if variable_labels is None:
            __, num_variables = record.sample.shape
            variable_labels = list(range(num_variables))

        return cls(record, variable_labels, info, vartype)