class_docstr: Abstract base task class.
  
  A task encapsulates metadata information such as the feature and
  target variable which to fit the data to and additional necessary
  instructions on how to fit and predict.
  
  Implements attributes and operations shared by all tasks,
  including compatibility checks between the concrete task type and
  passed metadata.
  
  Parameters
  ----------
  target : str
      The column name for the target variable to be predicted.
  features : list of str, optional, (default=None)
      The column name(s) for the feature variable. If None, every column
      apart from target will be used as a feature.
  metadata : pandas.DataFrame
      Contains the metadata that the task is expected to work with.
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docstr: Get parameter names for the task.
docstr_tok: ["Get","parameter","names","for","the","task","."]
code: def _get_param_names(cls):
      """Get parameter names for the task."""
      # fetch the constructor or the original constructor before
      # deprecation wrapping if any
      init = getattr(cls.__init__, "deprecated_original", cls.__init__)
      if init is object.__init__:
          # No explicit constructor to introspect
          return []
  
      # introspect the constructor arguments to find the model parameters
      # to represent
      init_signature = signature(init)
      # Consider the constructor parameters excluding 'self'
      parameters = [
          p
          for p in init_signature.parameters.values()
          if p.name != "self" and p.kind != p.VAR_KEYWORD
      ]
      # Extract and sort argument names excluding 'self'
      return sorted([p.name for p in parameters])
  
imports: from inspect import signature
  import numpy
  import pandas
  from sktime.base import BaseObject
label_desc: Use image node_id 7 for calling the BaseTask obj's underlying member method code with example usage: obj._get_param_names(cls) and returns: sorted, list