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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 create_ants_transform(transform_type='AffineTransform', precision='float', dimension=3, matrix=None, offset=None, center=None, translation=None, parameters=None, fixed_parameters=None, displacement_field=None, supported_types=False):
 """ Create and initialize an ANTsTransform ANTsR function: `createAntsrTransform` Arguments --------- transform_type : string type of transform(s) precision : string numerical precision dimension : integer spatial dimension of transform matrix : ndarray matrix for linear transforms offset : tuple/list offset for linear transforms center : tuple/list center for linear transforms translation : tuple/list translation for linear transforms parameters : ndarray/list array of parameters fixed_parameters : ndarray/list array of fixed parameters displacement_field : ANTsImage multichannel ANTsImage for non-linear transform supported_types : boolean flag that returns array of possible transforms types Returns ------- ANTsTransform or list of ANTsTransform types Example ------- """

    def _check_arg(arg, dim=1):
        if arg is None:
            if dim == 1:
                return []
            elif dim == 2:
                return [[]]
        elif isinstance(arg, np.ndarray):
            return arg.tolist()
        elif isinstance(arg, (tuple, list)):
            return list(arg)
        else:
            raise ValueError('Incompatible input argument')

    matrix = _check_arg(matrix, dim=2)
    offset = _check_arg(offset)
    center = _check_arg(center)
    translation = _check_arg(translation)
    parameters = _check_arg(parameters)
    fixed_parameters = _check_arg(fixed_parameters)

    matrix_offset_types = {'AffineTransform', 'CenteredAffineTransform', 
                         'Euler2DTransform', 'Euler3DTransform', 'Rigid3DTransform',
                         'Rigid2DTransform', 'QuaternionRigidTransform', 
                         'Similarity2DTransform', 'CenteredSimilarity2DTransform',
                         'Similarity3DTransform', 'CenteredRigid2DTransform', 
                         'CenteredEuler3DTransform'}

    #user_matrix_types = {'Affine','CenteredAffine', 
    #                     'Euler', 'CenteredEuler',
    #                     'Rigid', 'CenteredRigid', 'QuaternionRigid',
    #                     'Similarity', 'CenteredSimilarity'}

    if supported_types:
      return set(list(matrix_offset_types) + ['DisplacementFieldTransform'])

    # Check for valid dimension
    if (dimension < 2) or (dimension > 4):
        raise ValueError('Unsupported dimension: %i' % dimension)

    # Check for valid precision
    precision_types = ('float', 'double')
    if precision not in precision_types:
        raise ValueError('Unsupported Precision %s' % str(precision))

    # Check for supported transform type
    if (transform_type not in matrix_offset_types) and (transform_type != 'DisplacementFieldTransform'):
        raise ValueError('Unsupported type %s' % str(transform_type)) 

    # Check parameters with type
    if (transform_type=='Euler3DTransform'):
        dimension = 3
    elif (transform_type=='Euler2DTransform'):
        dimension = 2
    elif (transform_type=='Rigid3DTransform'):
        dimension = 3
    elif (transform_type=='QuaternionRigidTransform'):
        dimension = 3
    elif (transform_type=='Rigid2DTransform'):
        dimension = 2
    elif (transform_type=='CenteredRigid2DTransform'):
        dimension = 2
    elif (transform_type=='CenteredEuler3DTransform'):
        dimension = 3
    elif (transform_type=='Similarity3DTransform'):
        dimension = 3
    elif (transform_type=='Similarity2DTransform'):
        dimension = 2
    elif (transform_type=='CenteredSimilarity2DTransform'):
        dimension = 2

    # If displacement field
    if displacement_field is not None:
        raise ValueError('Displacement field transform not currently supported')
    #    itk_tx = transform_from_displacement_field(displacement_field)
    #    return tio.ants_transform(itk_tx)

    # Transforms that derive from itk::MatrixOffsetTransformBase
    libfn = utils.get_lib_fn('matrixOffset%s%i' % (utils.short_ptype(precision), dimension))
    itk_tx = libfn(transform_type,
                    precision,
                    dimension,
                    matrix,
                    offset,
                    center,
                    translation,
                    parameters,
                    fixed_parameters)
    return tio.ANTsTransform(precision=precision, dimension=dimension,
                            transform_type=transform_type, pointer=itk_tx)