parameters: (self,
                 model_dir=None,
                 label_dimension=1,
                 weight_column_name=None,
                 config=None,
                 feature_engineering_fn=None)
docstring: Initializes a DebugRegressor instance.
  
      Args:
        model_dir: Directory to save model parameters, graph and etc. This can
          also be used to load checkpoints from the directory into a estimator to
          continue training a previously saved model.
        label_dimension: Number of regression targets per example. This is the
          size of the last dimension of the labels and logits `Tensor` objects
          (typically, these have shape `[batch_size, label_dimension]`).
        weight_column_name: A string defining feature column name representing
          weights. It is used to down weight or boost examples during training. It
          will be multiplied by the loss of the example.
        config: `RunConfig` object to configure the runtime settings.
        feature_engineering_fn: Feature engineering function. Takes features and
                          labels which are the output of `input_fn` and returns
                          features and labels which will be fed into the model.
      Returns:
        A `DebugRegressor` estimator.
docstring_summary: Initializes a DebugRegressor instance.
docstring_tokens: Initializes a DebugRegressor instance .
function: def __init__(self,
                 model_dir=None,
                 label_dimension=1,
                 weight_column_name=None,
                 config=None,
                 feature_engineering_fn=None):
      """Initializes a DebugRegressor instance.
  
      Args:
        model_dir: Directory to save model parameters, graph and etc. This can
          also be used to load checkpoints from the directory into a estimator to
          continue training a previously saved model.
        label_dimension: Number of regression targets per example. This is the
          size of the last dimension of the labels and logits `Tensor` objects
          (typically, these have shape `[batch_size, label_dimension]`).
        weight_column_name: A string defining feature column name representing
          weights. It is used to down weight or boost examples during training. It
          will be multiplied by the loss of the example.
        config: `RunConfig` object to configure the runtime settings.
        feature_engineering_fn: Feature engineering function. Takes features and
                          labels which are the output of `input_fn` and returns
                          features and labels which will be fed into the model.
      Returns:
        A `DebugRegressor` estimator.
      """
  
      params = {
          "head":
              head_lib.regression_head(
                  weight_column_name=weight_column_name,
                  label_dimension=label_dimension,
                  enable_centered_bias=True)
      }
  
      super(DebugRegressor, self).__init__(
          model_fn=debug_model_fn,
          model_dir=model_dir,
          config=config,
          params=params,
          feature_engineering_fn=feature_engineering_fn)
function_tokens: def __init__ ( self , model_dir = None , label_dimension = 1 , weight_column_name = None , config = None , feature_engineering_fn = None ) : params = { "head" : head_lib . regression_head ( weight_column_name = weight_column_name , label_dimension = label_dimension , enable_centered_bias = True ) } super ( DebugRegressor , self ) . __init__ ( model_fn = debug_model_fn , model_dir = model_dir , config = config , params = params , feature_engineering_fn = feature_engineering_fn )