prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Fill Missing Op option to create a new binary column indicating the value was replaced.
  Fill Missing Op should have the option to create a new binary column indicating whether the column was filled or not for continuous variables.
  
  This is a common feature used when dealing with missing values of categoricals.
  
  </issue>
  <code>
  [start of nvtabular/ops/fill.py]
  1 #
  2 # Copyright (c) 2020, 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 cudf
  17 import dask_cudf
  18 from nvtx import annotate
  19 
  20 from .operator import ColumnNames, Operator
  21 from .stat_operator import StatOperator
  22 
  23 
  24 class FillMissing(Operator):
  25     """
  26     This operation replaces missing values with a constant pre-defined value
  27 
  28     Example usage::
  29 
  30         # Use FillMissing to define a workflow for continuous columns and specify the fill value
  31         # Default is 0
  32         cont_features = ['cont1', 'cont2', 'cont3'] >> ops.FillMissing() >> ...
  33         processor = nvtabular.Workflow(cont_features)
  34 
  35     Parameters
  36     -----------
  37     fill_val : float, default 0
  38         The constant value to replace missing values with.
  39     """
  40 
  41     def __init__(self, fill_val=0):
  42         super().__init__()
  43         self.fill_val = fill_val
  44 
  45     @annotate("FillMissing_op", color="darkgreen", domain="nvt_python")
  46     def transform(self, columns, gdf: cudf.DataFrame) -> cudf.DataFrame:
  47         return gdf[columns].fillna(self.fill_val)
  48 
  49     transform.__doc__ = Operator.transform.__doc__
  50 
  51 
  52 class FillMedian(StatOperator):
  53     """
  54     This operation replaces missing values with the median value for the column.
  55 
  56     Example usage::
  57 
  58         # Initialize the workflow
  59         proc = nvt.Workflow(
  60             cat_names=CATEGORICAL_COLUMNS,
  61             cont_names=CONTINUOUS_COLUMNS,
  62             label_name=LABEL_COLUMNS
  63         )
  64 
  65         # Add FillMedian to the workflow for continuous columns
  66         proc.add_cont_feature(nvt.ops.FillMedian())
  67     """
  68 
  69     def __init__(self):
  70         super().__init__()
  71         self.medians = {}
  72 
  73     @annotate("FillMedian_transform", color="darkgreen", domain="nvt_python")
  74     def transform(self, columns: ColumnNames, gdf: cudf.DataFrame) -> cudf.DataFrame:
  75         if not self.medians:
  76             raise RuntimeError("need to call 'fit' before running transform")
  77 
  78         for col in columns:
  79             gdf[col] = gdf[col].fillna(self.medians[col])
  80         return gdf
  81 
  82     @annotate("FillMedian_fit", color="green", domain="nvt_python")
  83     def fit(self, columns: ColumnNames, ddf: dask_cudf.DataFrame):
  84         # TODO: Use `method="tidigest"` when crick supports device
  85         dask_stats = ddf[columns].quantile(q=0.5, method="dask")
  86         return dask_stats
  87 
  88     @annotate("FillMedian_finalize", color="green", domain="nvt_python")
  89     def fit_finalize(self, dask_stats):
  90         for col in dask_stats.index.values_host:
  91             self.medians[col] = float(dask_stats[col])
  92 
  93     transform.__doc__ = Operator.transform.__doc__
  94     fit.__doc__ = StatOperator.fit.__doc__
  95     fit_finalize.__doc__ = StatOperator.fit_finalize.__doc__
  96 
  97     def clear(self):
  98         self.medians = {}
  99
  [end of nvtabular/ops/fill.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/nvtabular/ops/fill.py b/nvtabular/ops/fill.py
  --- a/nvtabular/ops/fill.py
  +++ b/nvtabular/ops/fill.py
  @@ -36,18 +36,34 @@
       -----------
       fill_val : float, default 0
           The constant value to replace missing values with.
  +    add_binary_cols : boolean, default False
  +        When True, adds binary columns that indicate whether cells in each column were filled
       """
   
  -    def __init__(self, fill_val=0):
  +    def __init__(self, fill_val=0, add_binary_cols=False):
           super().__init__()
           self.fill_val = fill_val
  +        self.add_binary_cols = add_binary_cols
   
       @annotate("FillMissing_op", color="darkgreen", domain="nvt_python")
       def transform(self, columns, gdf: cudf.DataFrame) -> cudf.DataFrame:
  -        return gdf[columns].fillna(self.fill_val)
  +        if self.add_binary_cols:
  +            for col in columns:
  +                gdf[f"{col}_filled"] = gdf[col].isna()
  +                gdf[col] = gdf[col].fillna(self.fill_val)
  +        else:
  +            gdf[columns] = gdf[columns].fillna(self.fill_val)
  +
  +        return gdf
   
       transform.__doc__ = Operator.transform.__doc__
   
  +    def output_column_names(self, columns: ColumnNames) -> ColumnNames:
  +        output_cols = columns[:]
  +        if self.add_binary_cols:
  +            output_cols.extend([f"{col}_filled" for col in columns])
  +        return output_cols
  +
   
   class FillMedian(StatOperator):
       """
  @@ -64,10 +80,16 @@
   
           # Add FillMedian to the workflow for continuous columns
           proc.add_cont_feature(nvt.ops.FillMedian())
  +
  +    Parameters
  +    -----------
  +    add_binary_cols : boolean, default False
  +        When True, adds binary columns that indicate whether cells in each column were filled
       """
   
  -    def __init__(self):
  +    def __init__(self, add_binary_cols=False):
           super().__init__()
  +        self.add_binary_cols = add_binary_cols
           self.medians = {}
   
       @annotate("FillMedian_transform", color="darkgreen", domain="nvt_python")
  @@ -76,6 +98,8 @@
               raise RuntimeError("need to call 'fit' before running transform")
   
           for col in columns:
  +            if self.add_binary_cols:
  +                gdf[f"{col}_filled"] = gdf[col].isna()
               gdf[col] = gdf[col].fillna(self.medians[col])
           return gdf
   
  @@ -96,3 +120,9 @@
   
       def clear(self):
           self.medians = {}
  +
  +    def output_column_names(self, columns: ColumnNames) -> ColumnNames:
  +        output_cols = columns[:]
  +        if self.add_binary_cols:
  +            output_cols.extend([f"{col}_filled" for col in columns])
  +        return output_cols
  
verification_info: {"golden_diff": "diff --git a/nvtabular/ops/fill.py b/nvtabular/ops/fill.py\n--- a/nvtabular/ops/fill.py\n+++ b/nvtabular/ops/fill.py\n@@ -36,18 +36,34 @@\n     -----------\n     fill_val : float, default 0\n         The constant value to replace missing values with.\n+    add_binary_cols : boolean, default False\n+        When True, adds binary columns that indicate whether cells in each column were filled\n     \"\"\"\n \n-    def __init__(self, fill_val=0):\n+    def __init__(self, fill_val=0, add_binary_cols=False):\n         super().__init__()\n         self.fill_val = fill_val\n+        self.add_binary_cols = add_binary_cols\n \n     @annotate(\"FillMissing_op\", color=\"darkgreen\", domain=\"nvt_python\")\n     def transform(self, columns, gdf: cudf.DataFrame) -> cudf.DataFrame:\n-        return gdf[columns].fillna(self.fill_val)\n+        if self.add_binary_cols:\n+            for col in columns:\n+                gdf[f\"{col}_filled\"] = gdf[col].isna()\n+                gdf[col] = gdf[col].fillna(self.fill_val)\n+        else:\n+            gdf[columns] = gdf[columns].fillna(self.fill_val)\n+\n+        return gdf\n \n     transform.__doc__ = Operator.transform.__doc__\n \n+    def output_column_names(self, columns: ColumnNames) -> ColumnNames:\n+        output_cols = columns[:]\n+        if self.add_binary_cols:\n+            output_cols.extend([f\"{col}_filled\" for col in columns])\n+        return output_cols\n+\n \n class FillMedian(StatOperator):\n     \"\"\"\n@@ -64,10 +80,16 @@\n \n         # Add FillMedian to the workflow for continuous columns\n         proc.add_cont_feature(nvt.ops.FillMedian())\n+\n+    Parameters\n+    -----------\n+    add_binary_cols : boolean, default False\n+        When True, adds binary columns that indicate whether cells in each column were filled\n     \"\"\"\n \n-    def __init__(self):\n+    def __init__(self, add_binary_cols=False):\n         super().__init__()\n+        self.add_binary_cols = add_binary_cols\n         self.medians = {}\n \n     @annotate(\"FillMedian_transform\", color=\"darkgreen\", domain=\"nvt_python\")\n@@ -76,6 +98,8 @@\n             raise RuntimeError(\"need to call 'fit' before running transform\")\n \n         for col in columns:\n+            if self.add_binary_cols:\n+                gdf[f\"{col}_filled\"] = gdf[col].isna()\n             gdf[col] = gdf[col].fillna(self.medians[col])\n         return gdf\n \n@@ -96,3 +120,9 @@\n \n     def clear(self):\n         self.medians = {}\n+\n+    def output_column_names(self, columns: ColumnNames) -> ColumnNames:\n+        output_cols = columns[:]\n+        if self.add_binary_cols:\n+            output_cols.extend([f\"{col}_filled\" for col in columns])\n+        return output_cols\n", "issue": "Fill Missing Op option to create a new binary column indicating the value was replaced.\nFill Missing Op should have the option to create a new binary column indicating whether the column was filled or not for continuous variables.\r\n\r\nThis is a common feature used when dealing with missing values of categoricals.\n", "before_files": [{"content": "#\n# Copyright (c) 2020, 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 cudf\nimport dask_cudf\nfrom nvtx import annotate\n\nfrom .operator import ColumnNames, Operator\nfrom .stat_operator import StatOperator\n\n\nclass FillMissing(Operator):\n    \"\"\"\n    This operation replaces missing values with a constant pre-defined value\n\n    Example usage::\n\n        # Use FillMissing to define a workflow for continuous columns and specify the fill value\n        # Default is 0\n        cont_features = ['cont1', 'cont2', 'cont3'] >> ops.FillMissing() >> ...\n        processor = nvtabular.Workflow(cont_features)\n\n    Parameters\n    -----------\n    fill_val : float, default 0\n        The constant value to replace missing values with.\n    \"\"\"\n\n    def __init__(self, fill_val=0):\n        super().__init__()\n        self.fill_val = fill_val\n\n    @annotate(\"FillMissing_op\", color=\"darkgreen\", domain=\"nvt_python\")\n    def transform(self, columns, gdf: cudf.DataFrame) -> cudf.DataFrame:\n        return gdf[columns].fillna(self.fill_val)\n\n    transform.__doc__ = Operator.transform.__doc__\n\n\nclass FillMedian(StatOperator):\n    \"\"\"\n    This operation replaces missing values with the median value for the column.\n\n    Example usage::\n\n        # Initialize the workflow\n        proc = nvt.Workflow(\n            cat_names=CATEGORICAL_COLUMNS,\n            cont_names=CONTINUOUS_COLUMNS,\n            label_name=LABEL_COLUMNS\n        )\n\n        # Add FillMedian to the workflow for continuous columns\n        proc.add_cont_feature(nvt.ops.FillMedian())\n    \"\"\"\n\n    def __init__(self):\n        super().__init__()\n        self.medians = {}\n\n    @annotate(\"FillMedian_transform\", color=\"darkgreen\", domain=\"nvt_python\")\n    def transform(self, columns: ColumnNames, gdf: cudf.DataFrame) -> cudf.DataFrame:\n        if not self.medians:\n            raise RuntimeError(\"need to call 'fit' before running transform\")\n\n        for col in columns:\n            gdf[col] = gdf[col].fillna(self.medians[col])\n        return gdf\n\n    @annotate(\"FillMedian_fit\", color=\"green\", domain=\"nvt_python\")\n    def fit(self, columns: ColumnNames, ddf: dask_cudf.DataFrame):\n        # TODO: Use `method=\"tidigest\"` when crick supports device\n        dask_stats = ddf[columns].quantile(q=0.5, method=\"dask\")\n        return dask_stats\n\n    @annotate(\"FillMedian_finalize\", color=\"green\", domain=\"nvt_python\")\n    def fit_finalize(self, dask_stats):\n        for col in dask_stats.index.values_host:\n            self.medians[col] = float(dask_stats[col])\n\n    transform.__doc__ = Operator.transform.__doc__\n    fit.__doc__ = StatOperator.fit.__doc__\n    fit_finalize.__doc__ = StatOperator.fit_finalize.__doc__\n\n    def clear(self):\n        self.medians = {}\n", "path": "nvtabular/ops/fill.py"}]}