docstr: Generate predictions for input samples.
  
  Parameters
  ----------
  x : ndarray
      Input samples.
  batch_size : int, optional
      Number of samples per batch.
  verbose : int, optional
      Verbosity mode.
  steps : int, optional
      Total number of steps (batches of samples) before declaring the
      prediction round finished.
  callbacks : list, optional
      List of Keras callbacks to apply during prediction.
  max_queue_size : int, optional
      Maximum size for the generator queue.
  workers : int, optional
      Maximum number of processes to spin up when using process-based
      threading.
  use_multiprocessing : bool, optional
      If `True`, use process-based threading. If `False`, use
      thread-based threading.
  
  Returns
  -------
  ndarray
      Numpy array of predictions.
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code: def predict(
      self,
      x,
      batch_size=None,
      verbose=0,
      steps=None,
      callbacks=None,
      max_queue_size=10,
      workers=1,
      use_multiprocessing=False,
  ):
      """Generate predictions for input samples.
  
      Parameters
      ----------
      x : ndarray
          Input samples.
      batch_size : int, optional
          Number of samples per batch.
      verbose : int, optional
          Verbosity mode.
      steps : int, optional
          Total number of steps (batches of samples) before declaring the
          prediction round finished.
      callbacks : list, optional
          List of Keras callbacks to apply during prediction.
      max_queue_size : int, optional
          Maximum size for the generator queue.
      workers : int, optional
          Maximum number of processes to spin up when using process-based
          threading.
      use_multiprocessing : bool, optional
          If `True`, use process-based threading. If `False`, use
          thread-based threading.
  
      Returns
      -------
      ndarray
          Numpy array of predictions.
  
      """
      sz = x.shape
      x = np.reshape(x, (sz[0] * sz[1] * sz[2], sz[3]))
      predicted_output = self.model.predict(
          x=x,
          batch_size=batch_size,
          verbose=verbose,
          steps=steps,
          callbacks=callbacks,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
      )
      predicted_output = np.float32(
          np.reshape(predicted_output, (sz[0], sz[1], sz[2], sz[3]))
      )
      return predicted_output
  
file_path: dipy/dipy/nn/cnn_1d_denoising.py
imports: from dipy.utils.optpkg import optional_package
  import numpy
label_desc: Use image node_id 7 for calling the Cnn1DDenoiser obj's underlying member method code with example usage: obj.predict(x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing) and returns: predicted_output