human
Write me a the Python program you requested given the following:
Complete the below
def all_fsl_pipeline(name=u'fsl_all_correct', epi_params=dict(echospacing=0.00077, acc_factor=3, enc_dir=u'y-'), altepi_params=dict(echospacing=0.00077, acc_factor=3, enc_dir=u'y')):

    ''''Workflow that integrates FSL ``topup`` and ``eddy``.
    .. warning:: this workflow rotates the gradients table (*b*-vectors)
    [Leemans09]_.
    .. warning:: this workflow does not perform jacobian modulation of each
    *DWI* [Jones10]_.
    Examples
    >>> from nipype.workflows.dmri.fsl.artifacts import all_fsl_pipeline
    >>> allcorr = all_fsl_pipeline()
    >>> allcorr.inputs.inputnode.in_file = \'epi.nii\'
    >>> allcorr.inputs.inputnode.alt_file = \'epi_rev.nii\'
    >>> allcorr.inputs.inputnode.in_bval = \'diffusion.bval\'
    >>> allcorr.inputs.inputnode.in_bvec = \'diffusion.bvec\'
    >>> allcorr.run() # doctest: +SKIP'
    '''
assistant
#Here is the Python program you requested.
    inputnode = pe.Node(niu.IdentityInterface(fields=[u'in_file', u'in_bvec', u'in_bval', u'alt_file']), name=u'inputnode')
    outputnode = pe.Node(niu.IdentityInterface(fields=[u'out_file', u'out_mask', u'out_bvec']), name=u'outputnode')
    def gen_index(in_file):
        import numpy as np
        import nibabel as nb
        import os
        from nipype.utils import NUMPY_MMAP
        out_file = os.path.abspath(u'index.txt')
        vols = nb.load(in_file, mmap=NUMPY_MMAP).get_data().shape[(-1)]
        np.savetxt(out_file, np.ones((vols,)).T)
        return out_file
    gen_idx = pe.Node(niu.Function(input_names=[u'in_file'], output_names=[u'out_file'], function=gen_index), name=u'gen_index')
    avg_b0_0 = pe.Node(niu.Function(input_names=[u'in_dwi', u'in_bval'], output_names=[u'out_file'], function=b0_average), name=u'b0_avg_pre')
    bet_dwi0 = pe.Node(fsl.BET(frac=0.3, mask=True, robust=True), name=u'bet_dwi_pre')
    sdc = sdc_peb(epi_params=epi_params, altepi_params=altepi_params)
    ecc = pe.Node(fsl.Eddy(method=u'jac'), name=u'fsl_eddy')
    rot_bvec = pe.Node(niu.Function(input_names=[u'in_bvec', u'eddy_params'], output_names=[u'out_file'], function=eddy_rotate_bvecs), name=u'Rotate_Bvec')
    avg_b0_1 = pe.Node(niu.Function(input_names=[u'in_dwi', u'in_bval'], output_names=[u'out_file'], function=b0_average), name=u'b0_avg_post')
    bet_dwi1 = pe.Node(fsl.BET(frac=0.3, mask=True, robust=True), name=u'bet_dwi_post')
    wf = pe.Workflow(name=name)
    wf.connect([(inputnode, avg_b0_0, [(u'in_file', u'in_dwi'), (u'in_bval', u'in_bval')]), (avg_b0_0, bet_dwi0, [(u'out_file', u'in_file')]), (bet_dwi0, sdc, [(u'mask_file', u'inputnode.in_mask')]), (inputnode, sdc, [(u'in_file', u'inputnode.in_file'), (u'alt_file', u'inputnode.alt_file'), (u'in_bval', u'inputnode.in_bval')]), (sdc, ecc, [(u'topup.out_enc_file', u'in_acqp'), (u'topup.out_fieldcoef', u'in_topup_fieldcoef'), (u'topup.out_movpar', u'in_topup_movpar')]), (bet_dwi0, ecc, [(u'mask_file', u'in_mask')]), (inputnode, gen_idx, [(u'in_file',...