class_docstr: Abstract base class for deep learning time series regression.
  
  The base classifier provides a deep learning default method for
  _predict, and provides a new abstract method for building a
  model.
  
  Parameters
  ----------
  batch_size : int, default = 40
      training batch size for the model
  
  Attributes
  ----------
  self.model_ - the fitted DL model
class_docstr_tok: ["Abstract","base","class","for","deep","learning","time","series","regression",".","The","base","classifier","provides","a","deep","learning","default","method","for","_predict",",","and","provides","a","new","abstract","method","for","building","a","model",".","Parameters","--","--","--","--","--","batch_size",":","int",",","default","=","40","training","batch","size","for","the","model","Attributes","--","--","--","--","--","self.model_","-","the","fitted","DL","model"]
docstr: Find regression estimate for all cases in X.
  
  Parameters
  ----------
  X : an np.ndarray of shape = (n_instances, n_dimensions, series_length)
      The training input samples.
  
  Returns
  -------
  predictions : 1d numpy array
      array of predictions of each instance
docstr_tok: ["Find","regression","estimate","for","all","cases","in","X.","Parameters","--","--","--","--","--","X",":","an","np.ndarray","of","shape","=","(","n_instances",",","n_dimensions",",","series_length",")","The","training","input","samples",".","Returns","--","--","--","-","predictions",":","1d","numpy","array","array","of","predictions","of","each","instance"]
code: def _predict(self, X, **kwargs):
      """Find regression estimate for all cases in X.
  
      Parameters
      ----------
      X : an np.ndarray of shape = (n_instances, n_dimensions, series_length)
          The training input samples.
  
      Returns
      -------
      predictions : 1d numpy array
          array of predictions of each instance
      """
      X = X.transpose((0, 2, 1))
      y_pred = self.model_.predict(X, self.batch_size, **kwargs)
      y_pred = np.squeeze(y_pred, axis=-1)
      return y_pred
  
file_path: sktime/sktime/regression/deep_learning/base.py
imports: from abc import ABC, abstractmethod
  import numpy
  from sktime.regression.base import BaseRegressor
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 3 for calling the BaseDeepRegressor obj's underlying member method code with example usage: obj._predict(X) and returns: y_pred