class_docstr: An abstract class representing a trainable model.
  
  The underlying model is trained by calling the `train` method with a
  training `Dataset`, producing a `Predictor` object.
code: def from_inputs(cls, train_iter, **params):
      # auto_params usually include `use_feat_dynamic_real`,
      # `use_feat_static_cat` and `cardinality`
      auto_params = cls.derive_auto_fields(train_iter)
      # user specified 'params' will take precedence:
      params = {**auto_params, **params}
      return cls.from_hyperparameters(**params)
  
file_path: gluonts/src/gluonts/model/estimator.py
imports: from typing import Optional
  from typing_extensions import Protocol, runtime_checkable
  import gluonts
  from gluonts.core.component import from_hyperparameters
  from gluonts.dataset.common import Dataset
  from gluonts.model.predictor import Predictor
label_desc: Use image node_id 5 for calling the Estimator obj's underlying member method code with example usage: obj.from_inputs(cls, train_iter) and returns: cls