class_docstr: Abstract base class for DeepChem models.
class_docstr_tok: ["Abstract","base","class","for","DeepChem","models","."]
docstr: Uses self to make predictions on provided Dataset object.
  
  Parameters
  ----------
  dataset: Dataset
      Dataset to make prediction on
  transformers: List[Transformer]
      Transformers that the input data has been transformed by. The output
      is passed through these transformers to undo the transformations.
  
  Returns
  -------
  np.ndarray
      A numpy array of predictions the model produces.
docstr_tok: ["Uses","self","to","make","predictions","on","provided","Dataset","object",".","Parameters","--","--","--","--","--","dataset",":","Dataset","Dataset","to","make","prediction","on","transformers",":","List","[","Transformer","]","Transformers","that","the","input","data","has","been","transformed","by",".","The","output","is","passed","through","these","transformers","to","undo","the","transformations",".","Returns","--","--","--","-","np.ndarray","A","numpy","array","of","predictions","the","model","produces","."]
code: def predict(
      self, dataset: Dataset, transformers: List[Transformer] = []
  ) -> OneOrMany[np.ndarray]:
      """
      Uses self to make predictions on provided Dataset object.
  
      Parameters
      ----------
      dataset: Dataset
          Dataset to make prediction on
      transformers: List[Transformer]
          Transformers that the input data has been transformed by. The output
          is passed through these transformers to undo the transformations.
  
      Returns
      -------
      np.ndarray
          A numpy array of predictions the model produces.
      """
      y_preds = []
      for X_batch, _, _, ids_batch in dataset.iterbatches(
          deterministic=True
      ):
          n_samples = len(X_batch)
          y_pred_batch = self.predict_on_batch(X_batch)
          # Discard any padded predictions
          y_pred_batch = y_pred_batch[:n_samples]
          y_pred_batch = undo_transforms(y_pred_batch, transformers)
          y_preds.append(y_pred_batch)
      y_pred = np.concatenate(y_preds)
      return y_pred
  
imports: import os
  import shutil
  import tempfile
  import logging
  from typing import List, Optional, Sequence
  import numpy
  from deepchem.data import Dataset
  from deepchem.metrics import Metric
  from deepchem.trans import Transformer, undo_transforms
  from deepchem.utils.evaluate import Evaluator
  from deepchem.utils.typing import OneOrMany
label_desc: Use image node_id 10 for calling the Model obj's underlying member method code with example usage: obj.predict(dataset, transformers) and returns: y_pred