docstr: Generates output predictions for the input samples.
  
  Computation is done in batches. This method is designed for batch
  processing of large numbers of inputs. It is not intended for use inside
  of loops that iterate over your data and process small numbers of inputs
  at a time.
  
  For small numbers of inputs that fit in one batch,
  directly use `__call__()` for faster execution, e.g.,
  `model(x)`, or `model(x, training=False)` if you have layers such as
  `BatchNormalization` that behave differently during
  inference.
  
  Note: See [this FAQ entry](
  https://keras.io/getting_started/faq/#whats-the-difference-between-model-methods-predict-and-call)
  for more details about the difference between `Model` methods
  `predict()` and `__call__()`.
  
  Args:
      x: Input samples. It could be:
          - A NumPy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
          - A tensor, or a list of tensors
              (in case the model has multiple inputs).
          - A `tf.data.Dataset`.
          - A `keras.utils.PyDataset` instance.
      batch_size: Integer or `None`.
          Number of samples per batch.
          If unspecified, `batch_size` will default to 32.
          Do not specify the `batch_size` if your data is in the
          form of dataset, generators, or `keras.utils.PyDataset`
          instances (since they generate batches).
      verbose: `"auto"`, 0, 1, or 2. Verbosity mode.
          0 = silent, 1 = progress bar, 2 = single line.
          `"auto"` becomes 1 for most cases. Note that the progress bar
          is not particularly useful when logged to a file,
          so `verbose=2` is recommended when not running interactively
          (e.g. in a production environment). Defaults to `"auto"`.
      steps: Total number of steps (batches of samples)
          before declaring the prediction round finished.
          Ignored with the default value of `None`.
          If `x` is a `tf.data.Dataset` and `steps` is `None`,
          `predict()` will run until the input dataset is exhausted.
      callbacks: List of `keras.callbacks.Callback` instances.
          List of callbacks to apply during prediction.
  
  Returns:
      NumPy array(s) of predictions.
code: def predict(
      self,
      x,
      batch_size=None,
      verbose="auto",
      steps=None,
      callbacks=None,
  ):
      """Generates output predictions for the input samples.
  
      Computation is done in batches. This method is designed for batch
      processing of large numbers of inputs. It is not intended for use inside
      of loops that iterate over your data and process small numbers of inputs
      at a time.
  
      For small numbers of inputs that fit in one batch,
      directly use `__call__()` for faster execution, e.g.,
      `model(x)`, or `model(x, training=False)` if you have layers such as
      `BatchNormalization` that behave differently during
      inference.
  
      Note: See [this FAQ entry](
      https://keras.io/getting_started/faq/#whats-the-difference-between-model-methods-predict-and-call)
      for more details about the difference between `Model` methods
      `predict()` and `__call__()`.
  
      Args:
          x: Input samples. It could be:
              - A NumPy array (or array-like), or a list of arrays
                  (in case the model has multiple inputs).
              - A tensor, or a list of tensors
                  (in case the model has multiple inputs).
              - A `tf.data.Dataset`.
              - A `keras.utils.PyDataset` instance.
          batch_size: Integer or `None`.
              Number of samples per batch.
              If unspecified, `batch_size` will default to 32.
              Do not specify the `batch_size` if your data is in the
              form of dataset, generators, or `keras.utils.PyDataset`
              instances (since they generate batches).
          verbose: `"auto"`, 0, 1, or 2. Verbosity mode.
              0 = silent, 1 = progress bar, 2 = single line.
              `"auto"` becomes 1 for most cases. Note that the progress bar
              is not particularly useful when logged to a file,
              so `verbose=2` is recommended when not running interactively
              (e.g. in a production environment). Defaults to `"auto"`.
          steps: Total number of steps (batches of samples)
              before declaring the prediction round finished.
              Ignored with the default value of `None`.
              If `x` is a `tf.data.Dataset` and `steps` is `None`,
              `predict()` will run until the input dataset is exhausted.
          callbacks: List of `keras.callbacks.Callback` instances.
              List of callbacks to apply during prediction.
  
      Returns:
          NumPy array(s) of predictions.
      """
      raise NotImplementedError
  
imports: import platform
  import warnings
  import tree
  from keras import backend
  from keras import metrics
  from keras import ops
  from keras import optimizers
  from keras.optimizers.loss_scale_optimizer import LossScaleOptimizer
  from keras.saving import serialization_lib
  from keras.trainers.compile_utils import CompileLoss
  from keras.trainers.compile_utils import CompileMetrics
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.utils import traceback_utils
  from keras.utils import tracking
label_desc: Use image node_id 17 for calling the Trainer obj's underlying member method code with example usage: obj.predict(x, batch_size, verbose, steps, callbacks) without return types