class_docstr: Regression Dense layers.
  
  The targets passing to the head would have to be tf.data.Dataset,
  np.ndarray, pd.DataFrame or pd.Series. It can be single-column or
  multi-column. The values should all be numerical.
  
  # Arguments
      output_dim: Int. The number of output dimensions. Defaults to None.
          If None, it will be inferred from the data.
      multi_label: Boolean. Defaults to False.
      loss: A Keras loss function. Defaults to use `mean_squared_error`.
      metrics: A list of Keras metrics. Defaults to use `mean_squared_error`.
      dropout: Float. The dropout rate for the layers.
          If left unspecified, it will be tuned automatically.
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code: def get_analyser(self):
      return analysers.RegressionAnalyser(
          name=self.name, output_dim=self.output_dim
      )
  
imports: from typing import Optional
  import tensorflow
  from tensorflow import keras
  from tensorflow import nest
  from tensorflow.keras import activations
  from tensorflow.keras import layers
  from tensorflow.keras import losses
  from autokeras import adapters
  from autokeras import analysers
  from autokeras import hyper_preprocessors
  from autokeras import preprocessors
  from autokeras.blocks import reduction
  from autokeras.engine import head
  from autokeras.utils import types
  from autokeras.utils import utils
label_desc: Use image node_id 6 for calling the RegressionHead obj's underlying member method code with example usage: obj.get_analyser() and returns: analysers