code: def regressor(
      model, X_train, X_test, y_train, y_test, model_name="Regressor"
  ):
      """Generates all sklearn regressor plots supported by W&B.
  
      The following plots are generated:
          learning curve, summary metrics, residuals plot, outlier candidates.
  
      Should only be called with a fitted regressor (otherwise an error is thrown).
  
      Arguments:
          model: (regressor) Takes in a fitted regressor.
          X_train: (arr) Training set features.
          y_train: (arr) Training set labels.
          X_test: (arr) Test set features.
          y_test: (arr) Test set labels.
          model_name: (str) Model name. Defaults to 'Regressor'
  
      Returns:
          None: To see plots, go to your W&B run page then expand the 'media' tab
              under 'auto visualizations'.
  
      Example:
      ```python
      wandb.sklearn.plot_regressor(reg, X_train, X_test, y_train, y_test, "Ridge")
      ```
      """
      wandb.termlog("\nPlotting %s." % model_name)
  
      shared.summary_metrics(model, X_train, y_train, X_test, y_test)
      wandb.termlog("Logged summary metrics.")
  
      shared.learning_curve(model, X_train, y_train)
      wandb.termlog("Logged learning curve.")
  
      outlier_candidates(model, X_train, y_train)
      wandb.termlog("Logged outlier candidates.")
  
      residuals(model, X_train, y_train)
      wandb.termlog("Logged residuals.")
  
file_path: wandb/wandb/sklearn/plot/regressor.py
imports: from warnings import simplefilter
  import numpy
  import wandb
  from wandb.sklearn import calculate, utils
  from .None import shared
label_desc: Use image node_id 1 for calling a global function with example usage: regressor(model, X_train, X_test, y_train, y_test, model_name) without return types