docstr: Uses self to make predictions on provided Dataset object.
  
  Returns:
    y_pred: numpy ndarray of shape (n_samples,)
docstr_tok: ["Uses","self","to","make","predictions","on","provided","Dataset","object",".","Returns",":","y_pred",":","numpy","ndarray","of","shape","(","n_samples",",",")"]
code: def predict(self, dataset, transformers=[]):
      """
      Uses self to make predictions on provided Dataset object.
  
      Returns:
        y_pred: numpy ndarray of shape (n_samples,)
      """
      y_preds = []
      n_tasks = self.n_tasks
      for X_batch, _, _, ids_batch in dataset.iterbatches(
          self.batch_size, deterministic=True
      ):
          n_samples = len(X_batch)
          y_pred_batch = self.predict_on_batch(X_batch)
          assert y_pred_batch.shape == (n_samples, n_tasks)
          y_pred_batch = undo_transforms(y_pred_batch, transformers)
          y_preds.append(y_pred_batch)
      y_pred = np.vstack(y_preds)
  
      # The iterbatches does padding with zero-weight examples on the last batch.
      # Remove padded examples.
      n_samples = len(dataset)
      y_pred = np.reshape(y_pred, (n_samples, n_tasks))
      # Special case to handle singletasks.
      if n_tasks == 1:
          y_pred = np.reshape(y_pred, (n_samples,))
      return y_pred
  
file_path: deepchem/contrib/torch/torch_model.py
imports: import torch
  import time
  import numpy
  from deepchem.trans import undo_transforms
  from deepchem.utils.save import log
  from deepchem.models import Model
label_desc: Use image node_id 5 for calling the TorchMultitaskModel obj's underlying member method code with example usage: obj.predict(dataset, transformers) and returns: y_pred