code: def _generate_dash_input(
      self, col, onehot_cols, onehot_dict, cat_dict
  ):
      if col in cat_dict:
          col_values = cat_dict[col]
          return html.Div(
              [
                  dbc.Label(col),
                  dcc.Dropdown(
                      id="feature-input-" + col + "-input-" + self.name,
                      options=[
                          dict(label=col_val, value=col_val)
                          for col_val in col_values
                      ],
                      style={"width": "100%"},
                      clearable=False,
                  ),
                  dbc.FormText(f"Select any {col}")
                  if not self.hide_range
                  else None,
              ]
          )
      elif col in onehot_cols:
          col_values = [c for c in onehot_dict[col]]
          display_values = [
              col_val[len(col) + 1 :]
              if col_val.startswith(col + "_")
              else col_val
              for col_val in col_values
          ]
          if any(
              self.explainer.X[self.explainer.onehot_dict[col]].sum(
                  axis=1
              )
              == 0
          ):
              col_values.append(self.explainer.onehot_notencoded[col])
              display_values.append(
                  self.explainer.onehot_notencoded[col]
              )
          return html.Div(
              [
                  dbc.Label(col),
                  dcc.Dropdown(
                      id="feature-input-" + col + "-input-" + self.name,
                      options=[
                          dict(label=display, value=col_val)
                          for display, col_val in zip(
                              display_values, col_values
                          )
                      ],
                      style={"width": "100%"},
                      clearable=False,
                  ),
                  dbc.FormText(f"Select any {col}")
                  if not self.hide_range
                  else None,
              ]
          )
      else:
          min_range = np.round(
              self.explainer.X[col][
                  lambda x: x != self.explainer.na_fill
              ].min(),
              2,
          )
          max_range = np.round(
              self.explainer.X[col][
                  lambda x: x != self.explainer.na_fill
              ].max(),
              2,
          )
          return html.Div(
              [
                  dbc.Label(col),
                  dbc.Input(
                      id="feature-input-" + col + "-input-" + self.name,
                      type="number",
                  ),
                  dbc.FormText(f"Range: {min_range}-{max_range}")
                  if not self.hide_range
                  else None,
              ]
          )
  
imports: from math import ceil
  import numpy
  import pandas
  import dash
  from dash import html, dcc, Input, Output, State, dash_table
  from dash.exceptions import PreventUpdate
  import dash_bootstrap_components
  from ..dashboard_methods import *
  from ..None import to_html
label_desc: Use image node_id 2 for calling the FeatureInputComponent obj's underlying member method code with example usage: obj._generate_dash_input(col, onehot_cols, onehot_dict, cat_dict) and returns: html, html, html