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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 write_spmatrix_to_sparse_tensor(file, array, labels=None):
 """Writes a scipy sparse matrix to a sparse tensor"""


    if not issparse(array):
        raise TypeError("Array must be sparse")

    # Validate shape of array and labels, resolve array and label types
    if not len(array.shape) == 2:
        raise ValueError("Array must be a Matrix")
    if labels is not None:
        if not len(labels.shape) == 1:
            raise ValueError("Labels must be a Vector")
        if labels.shape[0] not in array.shape:
            raise ValueError("Label shape {} not compatible with array shape {}".format(
                             labels.shape, array.shape))
        resolved_label_type = _resolve_type(labels.dtype)
    resolved_type = _resolve_type(array.dtype)

    csr_array = array.tocsr()
    n_rows, n_cols = csr_array.shape

    record = Record()
    for row_idx in range(n_rows):
        record.Clear()
        row = csr_array.getrow(row_idx)
        # Write values
        _write_feature_tensor(resolved_type, record, row.data)
        # Write keys
        _write_keys_tensor(resolved_type, record, row.indices.astype(np.uint64))

        # Write labels
        if labels is not None:
            _write_label_tensor(resolved_label_type, record, labels[row_idx])

        # Write shape
        _write_shape(resolved_type, record, n_cols)

        _write_recordio(file, record.SerializeToString())