We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
ICC: Cannot set the undefined 'sessions_F_map' attribute of a 'ICCOutputSpec' object
### Summary

Trying to run ICC raises a `TraitError` with the following description: "TraitError: Cannot set the undefined 'sessions_F_map' attribute of a 'ICCOutputSpec' object."
I may be doing things wrong, in which case a more informative error message would be welcome. Please look at the code snippet under _Script/Workflow Details_.

_Note:_ I do get maps (icc_map.nii, session_var_map.nii, subject_var_map.nii) as an output in my `os.getcwd()`.

### Actual behavior
ICC triggers an exception.

### Expected behavior
ICC runs smoothly.

### How to replicate the behavior
```bash
mkdir /Users/user/test
mv mask.nii.gz sub-{01,02}_ses-{01,02}.nii.gz /Users/user/test
# Where these niftis are sensible "ICC compatible" files
# And now run code pasted below
```
### Script/Workflow details

Please put URL to code or code here (if not too long).
```python
import os.path
from nipype.algorithms import icc

project_dir = '/Users/user/test/'

def fname(sub, ses):
    return os.path.join(project_dir, f'sub-{sub}_ses-{ses}.nii.gz')

lst = [[fname(1, 1), fname(1, 2)], 
       [fname(2, 1), fname(2, 2)]]
mask = os.path.join(project_dir, 'mask.nii.gz')

x = icc.ICC(subjects_sessions=lst, mask=mask)
x.run()
```

### Platform details:

Please paste the output of: `python -c "import nipype; print(nipype.get_info()); print(nipype.__version__)"`

```python
{'commit_hash': '%h',
 'commit_source': 'archive substitution',
 'networkx_version': '2.0',
 'nibabel_version': '2.2.1',
 'nipype_version': '1.0.0',
 'numpy_version': '1.13.3',
 'pkg_path': '/Users/user/anaconda3/lib/python3.6/site-packages/nipype',
 'scipy_version': '1.0.0',
 'sys_executable': '/Users/user/anaconda3/bin/python',
 'sys_platform': 'darwin',
 'sys_version': '3.6.4 | packaged by conda-forge | (default, Dec 23 2017, 16:54:01) \n[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)]',
 'traits_version': '4.6.0'}

```

### Execution environment

Choose one
- My python environment outside container


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `nipype/algorithms/icc.py`
Content:
```
1 # -*- coding: utf-8 -*-
2 from __future__ import (print_function, division, unicode_literals,
3                         absolute_import)
4 from builtins import range
5 import os
6 import numpy as np
7 from numpy import ones, kron, mean, eye, hstack, dot, tile
8 import nibabel as nb
9 from scipy.linalg import pinv
10 from ..interfaces.base import BaseInterfaceInputSpec, TraitedSpec, \
11     BaseInterface, traits, File
12 from ..utils import NUMPY_MMAP
13 
14 
15 class ICCInputSpec(BaseInterfaceInputSpec):
16     subjects_sessions = traits.List(
17         traits.List(File(exists=True)),
18         desc="n subjects m sessions 3D stat files",
19         mandatory=True)
20     mask = File(exists=True, mandatory=True)
21 
22 
23 class ICCOutputSpec(TraitedSpec):
24     icc_map = File(exists=True)
25     session_var_map = File(exists=True, desc="variance between sessions")
26     subject_var_map = File(exists=True, desc="variance between subjects")
27 
28 
29 class ICC(BaseInterface):
30     '''
31     Calculates Interclass Correlation Coefficient (3,1) as defined in
32     P. E. Shrout & Joseph L. Fleiss (1979). "Intraclass Correlations: Uses in
33     Assessing Rater Reliability". Psychological Bulletin 86 (2): 420-428. This
34     particular implementation is aimed at relaibility (test-retest) studies.
35     '''
36     input_spec = ICCInputSpec
37     output_spec = ICCOutputSpec
38 
39     def _run_interface(self, runtime):
40         maskdata = nb.load(self.inputs.mask).get_data()
41         maskdata = np.logical_not(
42             np.logical_or(maskdata == 0, np.isnan(maskdata)))
43 
44         session_datas = [[
45             nb.load(fname, mmap=NUMPY_MMAP).get_data()[maskdata].reshape(
46                 -1, 1) for fname in sessions
47         ] for sessions in self.inputs.subjects_sessions]
48         list_of_sessions = [
49             np.dstack(session_data) for session_data in session_datas
50         ]
51         all_data = np.hstack(list_of_sessions)
52         icc = np.zeros(session_datas[0][0].shape)
53         session_F = np.zeros(session_datas[0][0].shape)
54         session_var = np.zeros(session_datas[0][0].shape)
55         subject_var = np.zeros(session_datas[0][0].shape)
56 
57         for x in range(icc.shape[0]):
58             Y = all_data[x, :, :]
59             icc[x], subject_var[x], session_var[x], session_F[
60                 x], _, _ = ICC_rep_anova(Y)
61 
62         nim = nb.load(self.inputs.subjects_sessions[0][0])
63         new_data = np.zeros(nim.shape)
64         new_data[maskdata] = icc.reshape(-1, )
65         new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)
66         nb.save(new_img, 'icc_map.nii')
67 
68         new_data = np.zeros(nim.shape)
69         new_data[maskdata] = session_var.reshape(-1, )
70         new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)
71         nb.save(new_img, 'session_var_map.nii')
72 
73         new_data = np.zeros(nim.shape)
74         new_data[maskdata] = subject_var.reshape(-1, )
75         new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)
76         nb.save(new_img, 'subject_var_map.nii')
77 
78         return runtime
79 
80     def _list_outputs(self):
81         outputs = self._outputs().get()
82         outputs['icc_map'] = os.path.abspath('icc_map.nii')
83         outputs['sessions_F_map'] = os.path.abspath('sessions_F_map.nii')
84         outputs['session_var_map'] = os.path.abspath('session_var_map.nii')
85         outputs['subject_var_map'] = os.path.abspath('subject_var_map.nii')
86         return outputs
87 
88 
89 def ICC_rep_anova(Y):
90     '''
91     the data Y are entered as a 'table' ie subjects are in rows and repeated
92     measures in columns
93 
94     One Sample Repeated measure ANOVA
95 
96     Y = XB + E with X = [FaTor / Subjects]
97     '''
98 
99     [nb_subjects, nb_conditions] = Y.shape
100     dfc = nb_conditions - 1
101     dfe = (nb_subjects - 1) * dfc
102     dfr = nb_subjects - 1
103 
104     # Compute the repeated measure effect
105     # ------------------------------------
106 
107     # Sum Square Total
108     mean_Y = mean(Y)
109     SST = ((Y - mean_Y)**2).sum()
110 
111     # create the design matrix for the different levels
112     x = kron(eye(nb_conditions), ones((nb_subjects, 1)))  # sessions
113     x0 = tile(eye(nb_subjects), (nb_conditions, 1))  # subjects
114     X = hstack([x, x0])
115 
116     # Sum Square Error
117     predicted_Y = dot(dot(dot(X, pinv(dot(X.T, X))), X.T), Y.flatten('F'))
118     residuals = Y.flatten('F') - predicted_Y
119     SSE = (residuals**2).sum()
120 
121     residuals.shape = Y.shape
122 
123     MSE = SSE / dfe
124 
125     # Sum square session effect - between colums/sessions
126     SSC = ((mean(Y, 0) - mean_Y)**2).sum() * nb_subjects
127     MSC = SSC / dfc / nb_subjects
128 
129     session_effect_F = MSC / MSE
130 
131     # Sum Square subject effect - between rows/subjects
132     SSR = SST - SSC - SSE
133     MSR = SSR / dfr
134 
135     # ICC(3,1) = (mean square subjeT - mean square error) /
136     #            (mean square subjeT + (k-1)*-mean square error)
137     ICC = (MSR - MSE) / (MSR + dfc * MSE)
138 
139     e_var = MSE  # variance of error
140     r_var = (MSR - MSE) / nb_conditions  # variance between subjects
141 
142     return ICC, r_var, e_var, session_effect_F, dfc, dfe
143
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/nipype/algorithms/icc.py b/nipype/algorithms/icc.py
--- a/nipype/algorithms/icc.py
+++ b/nipype/algorithms/icc.py
@@ -80,7 +80,6 @@
     def _list_outputs(self):
         outputs = self._outputs().get()
         outputs['icc_map'] = os.path.abspath('icc_map.nii')
-        outputs['sessions_F_map'] = os.path.abspath('sessions_F_map.nii')
         outputs['session_var_map'] = os.path.abspath('session_var_map.nii')
         outputs['subject_var_map'] = os.path.abspath('subject_var_map.nii')
         return outputs

{"golden_diff": "diff --git a/nipype/algorithms/icc.py b/nipype/algorithms/icc.py\n--- a/nipype/algorithms/icc.py\n+++ b/nipype/algorithms/icc.py\n@@ -80,7 +80,6 @@\n     def _list_outputs(self):\n         outputs = self._outputs().get()\n         outputs['icc_map'] = os.path.abspath('icc_map.nii')\n-        outputs['sessions_F_map'] = os.path.abspath('sessions_F_map.nii')\n         outputs['session_var_map'] = os.path.abspath('session_var_map.nii')\n         outputs['subject_var_map'] = os.path.abspath('subject_var_map.nii')\n         return outputs\n", "issue": "ICC: Cannot set the undefined 'sessions_F_map' attribute of a 'ICCOutputSpec' object\n### Summary\r\n\r\nTrying to run ICC raises a `TraitError` with the following description: \"TraitError: Cannot set the undefined 'sessions_F_map' attribute of a 'ICCOutputSpec' object.\"\r\nI may be doing things wrong, in which case a more informative error message would be welcome. Please look at the code snippet under _Script/Workflow Details_.\r\n\r\n_Note:_ I do get maps (icc_map.nii, session_var_map.nii, subject_var_map.nii) as an output in my `os.getcwd()`.\r\n\r\n### Actual behavior\r\nICC triggers an exception.\r\n\r\n### Expected behavior\r\nICC runs smoothly.\r\n\r\n### How to replicate the behavior\r\n```bash\r\nmkdir /Users/user/test\r\nmv mask.nii.gz sub-{01,02}_ses-{01,02}.nii.gz /Users/user/test\r\n# Where these niftis are sensible \"ICC compatible\" files\r\n# And now run code pasted below\r\n```\r\n### Script/Workflow details\r\n\r\nPlease put URL to code or code here (if not too long).\r\n```python\r\nimport os.path\r\nfrom nipype.algorithms import icc\r\n\r\nproject_dir = '/Users/user/test/'\r\n\r\ndef fname(sub, ses):\r\n    return os.path.join(project_dir, f'sub-{sub}_ses-{ses}.nii.gz')\r\n\r\nlst = [[fname(1, 1), fname(1, 2)], \r\n       [fname(2, 1), fname(2, 2)]]\r\nmask = os.path.join(project_dir, 'mask.nii.gz')\r\n\r\nx = icc.ICC(subjects_sessions=lst, mask=mask)\r\nx.run()\r\n```\r\n\r\n### Platform details:\r\n\r\nPlease paste the output of: `python -c \"import nipype; print(nipype.get_info()); print(nipype.__version__)\"`\r\n\r\n```python\r\n{'commit_hash': '%h',\r\n 'commit_source': 'archive substitution',\r\n 'networkx_version': '2.0',\r\n 'nibabel_version': '2.2.1',\r\n 'nipype_version': '1.0.0',\r\n 'numpy_version': '1.13.3',\r\n 'pkg_path': '/Users/user/anaconda3/lib/python3.6/site-packages/nipype',\r\n 'scipy_version': '1.0.0',\r\n 'sys_executable': '/Users/user/anaconda3/bin/python',\r\n 'sys_platform': 'darwin',\r\n 'sys_version': '3.6.4 | packaged by conda-forge | (default, Dec 23 2017, 16:54:01) \\n[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)]',\r\n 'traits_version': '4.6.0'}\r\n\r\n```\r\n\r\n### Execution environment\r\n\r\nChoose one\r\n- My python environment outside container\r\n\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\nfrom __future__ import (print_function, division, unicode_literals,\n                        absolute_import)\nfrom builtins import range\nimport os\nimport numpy as np\nfrom numpy import ones, kron, mean, eye, hstack, dot, tile\nimport nibabel as nb\nfrom scipy.linalg import pinv\nfrom ..interfaces.base import BaseInterfaceInputSpec, TraitedSpec, \\\n    BaseInterface, traits, File\nfrom ..utils import NUMPY_MMAP\n\n\nclass ICCInputSpec(BaseInterfaceInputSpec):\n    subjects_sessions = traits.List(\n        traits.List(File(exists=True)),\n        desc=\"n subjects m sessions 3D stat files\",\n        mandatory=True)\n    mask = File(exists=True, mandatory=True)\n\n\nclass ICCOutputSpec(TraitedSpec):\n    icc_map = File(exists=True)\n    session_var_map = File(exists=True, desc=\"variance between sessions\")\n    subject_var_map = File(exists=True, desc=\"variance between subjects\")\n\n\nclass ICC(BaseInterface):\n    '''\n    Calculates Interclass Correlation Coefficient (3,1) as defined in\n    P. E. Shrout & Joseph L. Fleiss (1979). \"Intraclass Correlations: Uses in\n    Assessing Rater Reliability\". Psychological Bulletin 86 (2): 420-428. This\n    particular implementation is aimed at relaibility (test-retest) studies.\n    '''\n    input_spec = ICCInputSpec\n    output_spec = ICCOutputSpec\n\n    def _run_interface(self, runtime):\n        maskdata = nb.load(self.inputs.mask).get_data()\n        maskdata = np.logical_not(\n            np.logical_or(maskdata == 0, np.isnan(maskdata)))\n\n        session_datas = [[\n            nb.load(fname, mmap=NUMPY_MMAP).get_data()[maskdata].reshape(\n                -1, 1) for fname in sessions\n        ] for sessions in self.inputs.subjects_sessions]\n        list_of_sessions = [\n            np.dstack(session_data) for session_data in session_datas\n        ]\n        all_data = np.hstack(list_of_sessions)\n        icc = np.zeros(session_datas[0][0].shape)\n        session_F = np.zeros(session_datas[0][0].shape)\n        session_var = np.zeros(session_datas[0][0].shape)\n        subject_var = np.zeros(session_datas[0][0].shape)\n\n        for x in range(icc.shape[0]):\n            Y = all_data[x, :, :]\n            icc[x], subject_var[x], session_var[x], session_F[\n                x], _, _ = ICC_rep_anova(Y)\n\n        nim = nb.load(self.inputs.subjects_sessions[0][0])\n        new_data = np.zeros(nim.shape)\n        new_data[maskdata] = icc.reshape(-1, )\n        new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)\n        nb.save(new_img, 'icc_map.nii')\n\n        new_data = np.zeros(nim.shape)\n        new_data[maskdata] = session_var.reshape(-1, )\n        new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)\n        nb.save(new_img, 'session_var_map.nii')\n\n        new_data = np.zeros(nim.shape)\n        new_data[maskdata] = subject_var.reshape(-1, )\n        new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)\n        nb.save(new_img, 'subject_var_map.nii')\n\n        return runtime\n\n    def _list_outputs(self):\n        outputs = self._outputs().get()\n        outputs['icc_map'] = os.path.abspath('icc_map.nii')\n        outputs['sessions_F_map'] = os.path.abspath('sessions_F_map.nii')\n        outputs['session_var_map'] = os.path.abspath('session_var_map.nii')\n        outputs['subject_var_map'] = os.path.abspath('subject_var_map.nii')\n        return outputs\n\n\ndef ICC_rep_anova(Y):\n    '''\n    the data Y are entered as a 'table' ie subjects are in rows and repeated\n    measures in columns\n\n    One Sample Repeated measure ANOVA\n\n    Y = XB + E with X = [FaTor / Subjects]\n    '''\n\n    [nb_subjects, nb_conditions] = Y.shape\n    dfc = nb_conditions - 1\n    dfe = (nb_subjects - 1) * dfc\n    dfr = nb_subjects - 1\n\n    # Compute the repeated measure effect\n    # ------------------------------------\n\n    # Sum Square Total\n    mean_Y = mean(Y)\n    SST = ((Y - mean_Y)**2).sum()\n\n    # create the design matrix for the different levels\n    x = kron(eye(nb_conditions), ones((nb_subjects, 1)))  # sessions\n    x0 = tile(eye(nb_subjects), (nb_conditions, 1))  # subjects\n    X = hstack([x, x0])\n\n    # Sum Square Error\n    predicted_Y = dot(dot(dot(X, pinv(dot(X.T, X))), X.T), Y.flatten('F'))\n    residuals = Y.flatten('F') - predicted_Y\n    SSE = (residuals**2).sum()\n\n    residuals.shape = Y.shape\n\n    MSE = SSE / dfe\n\n    # Sum square session effect - between colums/sessions\n    SSC = ((mean(Y, 0) - mean_Y)**2).sum() * nb_subjects\n    MSC = SSC / dfc / nb_subjects\n\n    session_effect_F = MSC / MSE\n\n    # Sum Square subject effect - between rows/subjects\n    SSR = SST - SSC - SSE\n    MSR = SSR / dfr\n\n    # ICC(3,1) = (mean square subjeT - mean square error) /\n    #            (mean square subjeT + (k-1)*-mean square error)\n    ICC = (MSR - MSE) / (MSR + dfc * MSE)\n\n    e_var = MSE  # variance of error\n    r_var = (MSR - MSE) / nb_conditions  # variance between subjects\n\n    return ICC, r_var, e_var, session_effect_F, dfc, dfe\n", "path": "nipype/algorithms/icc.py"}], "after_files": [{"content": "# -*- coding: utf-8 -*-\nfrom __future__ import (print_function, division, unicode_literals,\n                        absolute_import)\nfrom builtins import range\nimport os\nimport numpy as np\nfrom numpy import ones, kron, mean, eye, hstack, dot, tile\nimport nibabel as nb\nfrom scipy.linalg import pinv\nfrom ..interfaces.base import BaseInterfaceInputSpec, TraitedSpec, \\\n    BaseInterface, traits, File\nfrom ..utils import NUMPY_MMAP\n\n\nclass ICCInputSpec(BaseInterfaceInputSpec):\n    subjects_sessions = traits.List(\n        traits.List(File(exists=True)),\n        desc=\"n subjects m sessions 3D stat files\",\n        mandatory=True)\n    mask = File(exists=True, mandatory=True)\n\n\nclass ICCOutputSpec(TraitedSpec):\n    icc_map = File(exists=True)\n    session_var_map = File(exists=True, desc=\"variance between sessions\")\n    subject_var_map = File(exists=True, desc=\"variance between subjects\")\n\n\nclass ICC(BaseInterface):\n    '''\n    Calculates Interclass Correlation Coefficient (3,1) as defined in\n    P. E. Shrout & Joseph L. Fleiss (1979). \"Intraclass Correlations: Uses in\n    Assessing Rater Reliability\". Psychological Bulletin 86 (2): 420-428. This\n    particular implementation is aimed at relaibility (test-retest) studies.\n    '''\n    input_spec = ICCInputSpec\n    output_spec = ICCOutputSpec\n\n    def _run_interface(self, runtime):\n        maskdata = nb.load(self.inputs.mask).get_data()\n        maskdata = np.logical_not(\n            np.logical_or(maskdata == 0, np.isnan(maskdata)))\n\n        session_datas = [[\n            nb.load(fname, mmap=NUMPY_MMAP).get_data()[maskdata].reshape(\n                -1, 1) for fname in sessions\n        ] for sessions in self.inputs.subjects_sessions]\n        list_of_sessions = [\n            np.dstack(session_data) for session_data in session_datas\n        ]\n        all_data = np.hstack(list_of_sessions)\n        icc = np.zeros(session_datas[0][0].shape)\n        session_F = np.zeros(session_datas[0][0].shape)\n        session_var = np.zeros(session_datas[0][0].shape)\n        subject_var = np.zeros(session_datas[0][0].shape)\n\n        for x in range(icc.shape[0]):\n            Y = all_data[x, :, :]\n            icc[x], subject_var[x], session_var[x], session_F[\n                x], _, _ = ICC_rep_anova(Y)\n\n        nim = nb.load(self.inputs.subjects_sessions[0][0])\n        new_data = np.zeros(nim.shape)\n        new_data[maskdata] = icc.reshape(-1, )\n        new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)\n        nb.save(new_img, 'icc_map.nii')\n\n        new_data = np.zeros(nim.shape)\n        new_data[maskdata] = session_var.reshape(-1, )\n        new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)\n        nb.save(new_img, 'session_var_map.nii')\n\n        new_data = np.zeros(nim.shape)\n        new_data[maskdata] = subject_var.reshape(-1, )\n        new_img = nb.Nifti1Image(new_data, nim.affine, nim.header)\n        nb.save(new_img, 'subject_var_map.nii')\n\n        return runtime\n\n    def _list_outputs(self):\n        outputs = self._outputs().get()\n        outputs['icc_map'] = os.path.abspath('icc_map.nii')\n        outputs['session_var_map'] = os.path.abspath('session_var_map.nii')\n        outputs['subject_var_map'] = os.path.abspath('subject_var_map.nii')\n        return outputs\n\n\ndef ICC_rep_anova(Y):\n    '''\n    the data Y are entered as a 'table' ie subjects are in rows and repeated\n    measures in columns\n\n    One Sample Repeated measure ANOVA\n\n    Y = XB + E with X = [FaTor / Subjects]\n    '''\n\n    [nb_subjects, nb_conditions] = Y.shape\n    dfc = nb_conditions - 1\n    dfe = (nb_subjects - 1) * dfc\n    dfr = nb_subjects - 1\n\n    # Compute the repeated measure effect\n    # ------------------------------------\n\n    # Sum Square Total\n    mean_Y = mean(Y)\n    SST = ((Y - mean_Y)**2).sum()\n\n    # create the design matrix for the different levels\n    x = kron(eye(nb_conditions), ones((nb_subjects, 1)))  # sessions\n    x0 = tile(eye(nb_subjects), (nb_conditions, 1))  # subjects\n    X = hstack([x, x0])\n\n    # Sum Square Error\n    predicted_Y = dot(dot(dot(X, pinv(dot(X.T, X))), X.T), Y.flatten('F'))\n    residuals = Y.flatten('F') - predicted_Y\n    SSE = (residuals**2).sum()\n\n    residuals.shape = Y.shape\n\n    MSE = SSE / dfe\n\n    # Sum square session effect - between colums/sessions\n    SSC = ((mean(Y, 0) - mean_Y)**2).sum() * nb_subjects\n    MSC = SSC / dfc / nb_subjects\n\n    session_effect_F = MSC / MSE\n\n    # Sum Square subject effect - between rows/subjects\n    SSR = SST - SSC - SSE\n    MSR = SSR / dfr\n\n    # ICC(3,1) = (mean square subjeT - mean square error) /\n    #            (mean square subjeT + (k-1)*-mean square error)\n    ICC = (MSR - MSE) / (MSR + dfc * MSE)\n\n    e_var = MSE  # variance of error\n    r_var = (MSR - MSE) / nb_conditions  # variance between subjects\n\n    return ICC, r_var, e_var, session_effect_F, dfc, dfe\n", "path": "nipype/algorithms/icc.py"}]}