prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [FEA] Create a padding option in the ListSlice Op
  The right padding option is required
  - to create fixed length lists generated from ETL workflow (see ([example nb](https://github.com/NVIDIA-Merlin/Transformers4Rec/blob/main/examples/getting-started-session-based/01-ETL-with-NVTabular.ipynb)). 
  -  to be able to serve a seq-to-seq model (ex. session-based model) to TIS with proper reshaped tensors and get the proper reshaped outputs.
  
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `nvtabular/ops/list_slice.py`
  Content:
  ```
  1 #
  2 # Copyright (c) 2021, NVIDIA CORPORATION.
  3 #
  4 # Licensed under the Apache License, Version 2.0 (the "License");
  5 # you may not use this file except in compliance with the License.
  6 # You may obtain a copy of the License at
  7 #
  8 #     http://www.apache.org/licenses/LICENSE-2.0
  9 #
  10 # Unless required by applicable law or agreed to in writing, software
  11 # distributed under the License is distributed on an "AS IS" BASIS,
  12 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13 # See the License for the specific language governing permissions and
  14 # limitations under the License.
  15 #
  16 import numba.cuda
  17 import numpy as np
  18 
  19 try:
  20     import cupy as cp
  21 except ImportError:
  22     cp = None
  23 
  24 from nvtabular.dispatch import DataFrameType, _build_cudf_list_column, _is_cpu_object, annotate
  25 
  26 from ..tags import Tags
  27 from .operator import ColumnSelector, Operator
  28 
  29 
  30 class ListSlice(Operator):
  31     """Slices a list column
  32 
  33     This operator provides the ability to slice list column by row. For example, to truncate a
  34     list column to only include the first 10 elements per row::
  35 
  36         truncated = column_names >> ops.ListSlice(10)
  37 
  38     Take the first 10 items, ignoring the first element::
  39 
  40         truncated = column_names >> ops.ListSlice(1, 11)
  41 
  42     Take the last 10 items from each row::
  43 
  44         truncated = column_names >> ops.ListSlice(-10)
  45     """
  46 
  47     def __init__(self, start, end=None):
  48         super().__init__()
  49         self.start = start
  50         self.end = end
  51 
  52         if self.start > 0 and self.end is None:
  53             self.end = self.start
  54             self.start = 0
  55 
  56         if self.end is None:
  57             self.end = np.iinfo(np.int64).max
  58 
  59     @annotate("ListSlice_op", color="darkgreen", domain="nvt_python")
  60     def transform(self, col_selector: ColumnSelector, df: DataFrameType) -> DataFrameType:
  61         on_cpu = _is_cpu_object(df)
  62         ret = type(df)()
  63         for col in col_selector.names:
  64             # handle CPU via normal python slicing (not very efficient)
  65             if on_cpu:
  66                 ret[col] = [row[self.start : self.end] for row in df[col]]
  67             else:
  68                 # figure out the size of each row from the list offsets
  69                 c = df[col]._column
  70                 offsets = c.offsets.values
  71                 elements = c.elements.values
  72 
  73                 # figure out the size of each row after slicing start/end
  74                 new_offsets = cp.zeros(offsets.size, dtype=offsets.dtype)
  75                 threads = 32
  76                 blocks = (offsets.size + threads - 1) // threads
  77 
  78                 # calculate new row offsets after slicing
  79                 _calculate_row_sizes[blocks, threads](self.start, self.end, offsets, new_offsets)
  80                 new_offsets = cp.cumsum(new_offsets).astype(offsets.dtype)
  81 
  82                 # create a new array for the sliced elements
  83                 new_elements = cp.zeros(new_offsets[-1].item(), dtype=elements.dtype)
  84                 if new_elements.size:
  85                     _slice_rows[blocks, threads](
  86                         self.start, offsets, elements, new_offsets, new_elements
  87                     )
  88 
  89                 # build up a list column with the sliced values
  90                 ret[col] = _build_cudf_list_column(new_elements, new_offsets)
  91 
  92         return ret
  93 
  94     def output_tags(self):
  95         return [Tags.LIST]
  96 
  97     transform.__doc__ = Operator.transform.__doc__
  98 
  99 
  100 @numba.cuda.jit
  101 def _calculate_row_sizes(start, end, offsets, row_sizes):
  102     """given a slice (start/end) and existing offsets indicating row lengths, this
  103     calculates the size for each new row after slicing"""
  104     rowid = numba.cuda.grid(1)
  105     if rowid < offsets.size - 1:
  106         original_row_size = offsets[rowid + 1] - offsets[rowid]
  107 
  108         # handle negative slicing appropriately
  109         if start < 0:
  110             start = original_row_size + start
  111         if end < 0:
  112             end = original_row_size + end
  113 
  114         # clamp start/end to be in (0, original_row_size)
  115         start = min(max(0, start), original_row_size)
  116         end = min(max(0, end), original_row_size)
  117 
  118         row_sizes[rowid + 1] = end - start
  119 
  120 
  121 @numba.cuda.jit
  122 def _slice_rows(start, offsets, elements, new_offsets, new_elements):
  123     """slices rows of a list column. requires the 'new_offsets' to
  124     be previously calculated (meaning that we don't need the 'end' slice index
  125     since that's baked into the new_offsets"""
  126     rowid = numba.cuda.grid(1)
  127     if rowid < (new_offsets.size - 1):
  128         if start >= 0:
  129             offset = offsets[rowid] + start
  130         else:
  131             offset = offsets[rowid + 1] + start
  132             if offset < offsets[rowid]:
  133                 offset = offsets[rowid]
  134 
  135         for new_offset in range(new_offsets[rowid], new_offsets[rowid + 1]):
  136             new_elements[new_offset] = elements[offset]
  137             offset += 1
  138
  ```
  --- 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()
  
  ```
  
golden_diff: diff --git a/nvtabular/ops/list_slice.py b/nvtabular/ops/list_slice.py
  --- a/nvtabular/ops/list_slice.py
  +++ b/nvtabular/ops/list_slice.py
  @@ -44,10 +44,12 @@
           truncated = column_names >> ops.ListSlice(-10)
       """
   
  -    def __init__(self, start, end=None):
  +    def __init__(self, start, end=None, pad=False, pad_value=0.0):
           super().__init__()
           self.start = start
           self.end = end
  +        self.pad = pad
  +        self.pad_value = pad_value
   
           if self.start > 0 and self.end is None:
               self.end = self.start
  @@ -60,27 +62,47 @@
       def transform(self, col_selector: ColumnSelector, df: DataFrameType) -> DataFrameType:
           on_cpu = _is_cpu_object(df)
           ret = type(df)()
  +
  +        max_elements = self.end - self.start
  +
           for col in col_selector.names:
               # handle CPU via normal python slicing (not very efficient)
               if on_cpu:
  -                ret[col] = [row[self.start : self.end] for row in df[col]]
  +                values = [row[self.start : self.end] for row in df[col]]
  +
  +                # pad out to so each row has max_elements if askeed
  +                if self.pad:
  +                    for v in values:
  +                        if len(v) < max_elements:
  +                            v.extend([self.pad_value] * (max_elements - len(v)))
  +
  +                ret[col] = values
               else:
                   # figure out the size of each row from the list offsets
                   c = df[col]._column
                   offsets = c.offsets.values
                   elements = c.elements.values
   
  -                # figure out the size of each row after slicing start/end
  -                new_offsets = cp.zeros(offsets.size, dtype=offsets.dtype)
                   threads = 32
                   blocks = (offsets.size + threads - 1) // threads
   
  -                # calculate new row offsets after slicing
  -                _calculate_row_sizes[blocks, threads](self.start, self.end, offsets, new_offsets)
  -                new_offsets = cp.cumsum(new_offsets).astype(offsets.dtype)
  +                if self.pad:
  +                    new_offsets = cp.arange(offsets.size, dtype=offsets.dtype) * max_elements
  +
  +                else:
  +                    # figure out the size of each row after slicing start/end
  +                    new_offsets = cp.zeros(offsets.size, dtype=offsets.dtype)
  +
  +                    # calculate new row offsets after slicing
  +                    _calculate_row_sizes[blocks, threads](
  +                        self.start, self.end, offsets, new_offsets
  +                    )
  +                    new_offsets = cp.cumsum(new_offsets).astype(offsets.dtype)
   
                   # create a new array for the sliced elements
  -                new_elements = cp.zeros(new_offsets[-1].item(), dtype=elements.dtype)
  +                new_elements = cp.full(
  +                    new_offsets[-1].item(), fill_value=self.pad_value, dtype=elements.dtype
  +                )
                   if new_elements.size:
                       _slice_rows[blocks, threads](
                           self.start, offsets, elements, new_offsets, new_elements
  @@ -132,6 +154,15 @@
               if offset < offsets[rowid]:
                   offset = offsets[rowid]
   
  -        for new_offset in range(new_offsets[rowid], new_offsets[rowid + 1]):
  +        new_start = new_offsets[rowid]
  +        new_end = new_offsets[rowid + 1]
  +
  +        # if we are padding (more new offsets than old olffsets) - don't keep on iterating past
  +        # the end
  +        offset_delta = (new_end - new_start) - (offsets[rowid + 1] - offset)
  +        if offset_delta > 0:
  +            new_end -= offset_delta
  +
  +        for new_offset in range(new_start, new_end):
               new_elements[new_offset] = elements[offset]
               offset += 1
  
verification_info: {"golden_diff": "diff --git a/nvtabular/ops/list_slice.py b/nvtabular/ops/list_slice.py\n--- a/nvtabular/ops/list_slice.py\n+++ b/nvtabular/ops/list_slice.py\n@@ -44,10 +44,12 @@\n         truncated = column_names >> ops.ListSlice(-10)\n     \"\"\"\n \n-    def __init__(self, start, end=None):\n+    def __init__(self, start, end=None, pad=False, pad_value=0.0):\n         super().__init__()\n         self.start = start\n         self.end = end\n+        self.pad = pad\n+        self.pad_value = pad_value\n \n         if self.start > 0 and self.end is None:\n             self.end = self.start\n@@ -60,27 +62,47 @@\n     def transform(self, col_selector: ColumnSelector, df: DataFrameType) -> DataFrameType:\n         on_cpu = _is_cpu_object(df)\n         ret = type(df)()\n+\n+        max_elements = self.end - self.start\n+\n         for col in col_selector.names:\n             # handle CPU via normal python slicing (not very efficient)\n             if on_cpu:\n-                ret[col] = [row[self.start : self.end] for row in df[col]]\n+                values = [row[self.start : self.end] for row in df[col]]\n+\n+                # pad out to so each row has max_elements if askeed\n+                if self.pad:\n+                    for v in values:\n+                        if len(v) < max_elements:\n+                            v.extend([self.pad_value] * (max_elements - len(v)))\n+\n+                ret[col] = values\n             else:\n                 # figure out the size of each row from the list offsets\n                 c = df[col]._column\n                 offsets = c.offsets.values\n                 elements = c.elements.values\n \n-                # figure out the size of each row after slicing start/end\n-                new_offsets = cp.zeros(offsets.size, dtype=offsets.dtype)\n                 threads = 32\n                 blocks = (offsets.size + threads - 1) // threads\n \n-                # calculate new row offsets after slicing\n-                _calculate_row_sizes[blocks, threads](self.start, self.end, offsets, new_offsets)\n-                new_offsets = cp.cumsum(new_offsets).astype(offsets.dtype)\n+                if self.pad:\n+                    new_offsets = cp.arange(offsets.size, dtype=offsets.dtype) * max_elements\n+\n+                else:\n+                    # figure out the size of each row after slicing start/end\n+                    new_offsets = cp.zeros(offsets.size, dtype=offsets.dtype)\n+\n+                    # calculate new row offsets after slicing\n+                    _calculate_row_sizes[blocks, threads](\n+                        self.start, self.end, offsets, new_offsets\n+                    )\n+                    new_offsets = cp.cumsum(new_offsets).astype(offsets.dtype)\n \n                 # create a new array for the sliced elements\n-                new_elements = cp.zeros(new_offsets[-1].item(), dtype=elements.dtype)\n+                new_elements = cp.full(\n+                    new_offsets[-1].item(), fill_value=self.pad_value, dtype=elements.dtype\n+                )\n                 if new_elements.size:\n                     _slice_rows[blocks, threads](\n                         self.start, offsets, elements, new_offsets, new_elements\n@@ -132,6 +154,15 @@\n             if offset < offsets[rowid]:\n                 offset = offsets[rowid]\n \n-        for new_offset in range(new_offsets[rowid], new_offsets[rowid + 1]):\n+        new_start = new_offsets[rowid]\n+        new_end = new_offsets[rowid + 1]\n+\n+        # if we are padding (more new offsets than old olffsets) - don't keep on iterating past\n+        # the end\n+        offset_delta = (new_end - new_start) - (offsets[rowid + 1] - offset)\n+        if offset_delta > 0:\n+            new_end -= offset_delta\n+\n+        for new_offset in range(new_start, new_end):\n             new_elements[new_offset] = elements[offset]\n             offset += 1\n", "issue": "[FEA] Create a padding option in the ListSlice Op\nThe right padding option is required\r\n- to create fixed length lists generated from ETL workflow (see ([example nb](https://github.com/NVIDIA-Merlin/Transformers4Rec/blob/main/examples/getting-started-session-based/01-ETL-with-NVTabular.ipynb)). \r\n-  to be able to serve a seq-to-seq model (ex. session-based model) to TIS with proper reshaped tensors and get the proper reshaped outputs.\r\n\r\n\n", "before_files": [{"content": "#\n# Copyright (c) 2021, NVIDIA CORPORATION.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nimport numba.cuda\nimport numpy as np\n\ntry:\n    import cupy as cp\nexcept ImportError:\n    cp = None\n\nfrom nvtabular.dispatch import DataFrameType, _build_cudf_list_column, _is_cpu_object, annotate\n\nfrom ..tags import Tags\nfrom .operator import ColumnSelector, Operator\n\n\nclass ListSlice(Operator):\n    \"\"\"Slices a list column\n\n    This operator provides the ability to slice list column by row. For example, to truncate a\n    list column to only include the first 10 elements per row::\n\n        truncated = column_names >> ops.ListSlice(10)\n\n    Take the first 10 items, ignoring the first element::\n\n        truncated = column_names >> ops.ListSlice(1, 11)\n\n    Take the last 10 items from each row::\n\n        truncated = column_names >> ops.ListSlice(-10)\n    \"\"\"\n\n    def __init__(self, start, end=None):\n        super().__init__()\n        self.start = start\n        self.end = end\n\n        if self.start > 0 and self.end is None:\n            self.end = self.start\n            self.start = 0\n\n        if self.end is None:\n            self.end = np.iinfo(np.int64).max\n\n    @annotate(\"ListSlice_op\", color=\"darkgreen\", domain=\"nvt_python\")\n    def transform(self, col_selector: ColumnSelector, df: DataFrameType) -> DataFrameType:\n        on_cpu = _is_cpu_object(df)\n        ret = type(df)()\n        for col in col_selector.names:\n            # handle CPU via normal python slicing (not very efficient)\n            if on_cpu:\n                ret[col] = [row[self.start : self.end] for row in df[col]]\n            else:\n                # figure out the size of each row from the list offsets\n                c = df[col]._column\n                offsets = c.offsets.values\n                elements = c.elements.values\n\n                # figure out the size of each row after slicing start/end\n                new_offsets = cp.zeros(offsets.size, dtype=offsets.dtype)\n                threads = 32\n                blocks = (offsets.size + threads - 1) // threads\n\n                # calculate new row offsets after slicing\n                _calculate_row_sizes[blocks, threads](self.start, self.end, offsets, new_offsets)\n                new_offsets = cp.cumsum(new_offsets).astype(offsets.dtype)\n\n                # create a new array for the sliced elements\n                new_elements = cp.zeros(new_offsets[-1].item(), dtype=elements.dtype)\n                if new_elements.size:\n                    _slice_rows[blocks, threads](\n                        self.start, offsets, elements, new_offsets, new_elements\n                    )\n\n                # build up a list column with the sliced values\n                ret[col] = _build_cudf_list_column(new_elements, new_offsets)\n\n        return ret\n\n    def output_tags(self):\n        return [Tags.LIST]\n\n    transform.__doc__ = Operator.transform.__doc__\n\n\n@numba.cuda.jit\ndef _calculate_row_sizes(start, end, offsets, row_sizes):\n    \"\"\"given a slice (start/end) and existing offsets indicating row lengths, this\n    calculates the size for each new row after slicing\"\"\"\n    rowid = numba.cuda.grid(1)\n    if rowid < offsets.size - 1:\n        original_row_size = offsets[rowid + 1] - offsets[rowid]\n\n        # handle negative slicing appropriately\n        if start < 0:\n            start = original_row_size + start\n        if end < 0:\n            end = original_row_size + end\n\n        # clamp start/end to be in (0, original_row_size)\n        start = min(max(0, start), original_row_size)\n        end = min(max(0, end), original_row_size)\n\n        row_sizes[rowid + 1] = end - start\n\n\n@numba.cuda.jit\ndef _slice_rows(start, offsets, elements, new_offsets, new_elements):\n    \"\"\"slices rows of a list column. requires the 'new_offsets' to\n    be previously calculated (meaning that we don't need the 'end' slice index\n    since that's baked into the new_offsets\"\"\"\n    rowid = numba.cuda.grid(1)\n    if rowid < (new_offsets.size - 1):\n        if start >= 0:\n            offset = offsets[rowid] + start\n        else:\n            offset = offsets[rowid + 1] + start\n            if offset < offsets[rowid]:\n                offset = offsets[rowid]\n\n        for new_offset in range(new_offsets[rowid], new_offsets[rowid + 1]):\n            new_elements[new_offset] = elements[offset]\n            offset += 1\n", "path": "nvtabular/ops/list_slice.py"}], "after_files": [{"content": "#\n# Copyright (c) 2021, NVIDIA CORPORATION.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nimport numba.cuda\nimport numpy as np\n\ntry:\n    import cupy as cp\nexcept ImportError:\n    cp = None\n\nfrom nvtabular.dispatch import DataFrameType, _build_cudf_list_column, _is_cpu_object, annotate\n\nfrom ..tags import Tags\nfrom .operator import ColumnSelector, Operator\n\n\nclass ListSlice(Operator):\n    \"\"\"Slices a list column\n\n    This operator provides the ability to slice list column by row. For example, to truncate a\n    list column to only include the first 10 elements per row::\n\n        truncated = column_names >> ops.ListSlice(10)\n\n    Take the first 10 items, ignoring the first element::\n\n        truncated = column_names >> ops.ListSlice(1, 11)\n\n    Take the last 10 items from each row::\n\n        truncated = column_names >> ops.ListSlice(-10)\n    \"\"\"\n\n    def __init__(self, start, end=None, pad=False, pad_value=0.0):\n        super().__init__()\n        self.start = start\n        self.end = end\n        self.pad = pad\n        self.pad_value = pad_value\n\n        if self.start > 0 and self.end is None:\n            self.end = self.start\n            self.start = 0\n\n        if self.end is None:\n            self.end = np.iinfo(np.int64).max\n\n    @annotate(\"ListSlice_op\", color=\"darkgreen\", domain=\"nvt_python\")\n    def transform(self, col_selector: ColumnSelector, df: DataFrameType) -> DataFrameType:\n        on_cpu = _is_cpu_object(df)\n        ret = type(df)()\n\n        max_elements = self.end - self.start\n\n        for col in col_selector.names:\n            # handle CPU via normal python slicing (not very efficient)\n            if on_cpu:\n                values = [row[self.start : self.end] for row in df[col]]\n\n                # pad out to so each row has max_elements if askeed\n                if self.pad:\n                    for v in values:\n                        if len(v) < max_elements:\n                            v.extend([self.pad_value] * (max_elements - len(v)))\n\n                ret[col] = values\n            else:\n                # figure out the size of each row from the list offsets\n                c = df[col]._column\n                offsets = c.offsets.values\n                elements = c.elements.values\n\n                threads = 32\n                blocks = (offsets.size + threads - 1) // threads\n\n                if self.pad:\n                    new_offsets = cp.arange(offsets.size, dtype=offsets.dtype) * max_elements\n\n                else:\n                    # figure out the size of each row after slicing start/end\n                    new_offsets = cp.zeros(offsets.size, dtype=offsets.dtype)\n\n                    # calculate new row offsets after slicing\n                    _calculate_row_sizes[blocks, threads](\n                        self.start, self.end, offsets, new_offsets\n                    )\n                    new_offsets = cp.cumsum(new_offsets).astype(offsets.dtype)\n\n                # create a new array for the sliced elements\n                new_elements = cp.full(\n                    new_offsets[-1].item(), fill_value=self.pad_value, dtype=elements.dtype\n                )\n                if new_elements.size:\n                    _slice_rows[blocks, threads](\n                        self.start, offsets, elements, new_offsets, new_elements\n                    )\n\n                # build up a list column with the sliced values\n                ret[col] = _build_cudf_list_column(new_elements, new_offsets)\n\n        return ret\n\n    def output_tags(self):\n        return [Tags.LIST]\n\n    transform.__doc__ = Operator.transform.__doc__\n\n\n@numba.cuda.jit\ndef _calculate_row_sizes(start, end, offsets, row_sizes):\n    \"\"\"given a slice (start/end) and existing offsets indicating row lengths, this\n    calculates the size for each new row after slicing\"\"\"\n    rowid = numba.cuda.grid(1)\n    if rowid < offsets.size - 1:\n        original_row_size = offsets[rowid + 1] - offsets[rowid]\n\n        # handle negative slicing appropriately\n        if start < 0:\n            start = original_row_size + start\n        if end < 0:\n            end = original_row_size + end\n\n        # clamp start/end to be in (0, original_row_size)\n        start = min(max(0, start), original_row_size)\n        end = min(max(0, end), original_row_size)\n\n        row_sizes[rowid + 1] = end - start\n\n\n@numba.cuda.jit\ndef _slice_rows(start, offsets, elements, new_offsets, new_elements):\n    \"\"\"slices rows of a list column. requires the 'new_offsets' to\n    be previously calculated (meaning that we don't need the 'end' slice index\n    since that's baked into the new_offsets\"\"\"\n    rowid = numba.cuda.grid(1)\n    if rowid < (new_offsets.size - 1):\n        if start >= 0:\n            offset = offsets[rowid] + start\n        else:\n            offset = offsets[rowid + 1] + start\n            if offset < offsets[rowid]:\n                offset = offsets[rowid]\n\n        new_start = new_offsets[rowid]\n        new_end = new_offsets[rowid + 1]\n\n        # if we are padding (more new offsets than old olffsets) - don't keep on iterating past\n        # the end\n        offset_delta = (new_end - new_start) - (offsets[rowid + 1] - offset)\n        if offset_delta > 0:\n            new_end -= offset_delta\n\n        for new_offset in range(new_start, new_end):\n            new_elements[new_offset] = elements[offset]\n            offset += 1\n", "path": "nvtabular/ops/list_slice.py"}]}