class_docstr: Performs binary-classification or regression prediction from input vectors.
  
  The implementation of
  :meth:`~sagemaker.predictor.Predictor.predict` in this
  `Predictor` requires a numpy ``ndarray`` as input. The array should
  contain the same number of columns as the feature-dimension of the data used
  to fit the model this Predictor performs inference on.
  
  :func:`predict` returns a list of
  :class:`~sagemaker.amazon.record_pb2.Record` objects (assuming the default
  recordio-protobuf ``deserializer`` is used), one for each row in
  the input ``ndarray``. The prediction is stored in the ``"predicted_label"``
  key of the ``Record.label`` field.
docstr: Initialization for LinearLearnerPredictor.
  
  Args:
      endpoint_name (str): Name of the Amazon SageMaker endpoint to which
          requests are sent.
      sagemaker_session (sagemaker.session.Session): A SageMaker Session
          object, used for SageMaker interactions (default: None). If not
          specified, one is created using the default AWS configuration
          chain.
      serializer (sagemaker.serializers.BaseSerializer): Optional. Default
          serializes input data to x-recordio-protobuf format.
      deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
          Default parses responses from x-recordio-protobuf format.
      component_name (str): Optional. Name of the Amazon SageMaker inference
          component corresponding to the predictor.
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code: def __init__(
      self,
      endpoint_name,
      sagemaker_session=None,
      serializer=RecordSerializer(),
      deserializer=RecordDeserializer(),
      component_name=None,
  ):
      """Initialization for LinearLearnerPredictor.
  
      Args:
          endpoint_name (str): Name of the Amazon SageMaker endpoint to which
              requests are sent.
          sagemaker_session (sagemaker.session.Session): A SageMaker Session
              object, used for SageMaker interactions (default: None). If not
              specified, one is created using the default AWS configuration
              chain.
          serializer (sagemaker.serializers.BaseSerializer): Optional. Default
              serializes input data to x-recordio-protobuf format.
          deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
              Default parses responses from x-recordio-protobuf format.
          component_name (str): Optional. Name of the Amazon SageMaker inference
              component corresponding to the predictor.
      """
      super(LinearLearnerPredictor, self).__init__(
          endpoint_name,
          sagemaker_session,
          serializer=serializer,
          deserializer=deserializer,
          component_name=component_name,
      )
  
imports: from __future__ import absolute_import
  import logging
  from typing import Union, Optional
  from sagemaker import image_uris
  from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase
  from sagemaker.amazon.common import RecordSerializer, RecordDeserializer
  from sagemaker.amazon.hyperparameter import Hyperparameter
  from sagemaker.amazon.validation import isin, gt, lt, ge, le
  from sagemaker.predictor import Predictor
  from sagemaker.model import Model
  from sagemaker.session import Session
  from sagemaker.utils import pop_out_unused_kwarg
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 1 to create a new LinearLearnerPredictor object from inherited base classes: Predictor with example: obj = LinearLearnerPredictor(endpoint_name, sagemaker_session, serializer, deserializer, component_name)