class_docstr: Returns dot product of entity and IP address embeddings as a score for compatibility.
  
  The implementation of
  :meth:`~sagemaker.predictor.Predictor.predict` in this
  `Predictor` requires a numpy ``ndarray`` as input. The array should
  contain two columns. The first column should contain the entity ID. The
  second column should contain the IPv4 address in dot notation.
docstr: Creates object to be used to get dot product of entity nad IP address.
  
  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 text/csv.
      deserializer (callable): Optional. Default parses JSON responses
          using ``json.load(...)``.
      component_name (str): Optional. Name of the Amazon SageMaker inference
          component corresponding the predictor.
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code: def __init__(
      self,
      endpoint_name,
      sagemaker_session=None,
      serializer=CSVSerializer(),
      deserializer=JSONDeserializer(),
      component_name=None,
  ):
      """Creates object to be used to get dot product of entity nad IP address.
  
      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 text/csv.
          deserializer (callable): Optional. Default parses JSON responses
              using ``json.load(...)``.
          component_name (str): Optional. Name of the Amazon SageMaker inference
              component corresponding the predictor.
      """
      super(IPInsightsPredictor, self).__init__(
          endpoint_name,
          sagemaker_session,
          serializer=serializer,
          deserializer=deserializer,
          component_name=component_name,
      )
  
imports: from __future__ import absolute_import
  from typing import Union, Optional
  from sagemaker import image_uris
  from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase
  from sagemaker.amazon.hyperparameter import Hyperparameter
  from sagemaker.amazon.validation import ge, le
  from sagemaker.deserializers import JSONDeserializer
  from sagemaker.predictor import Predictor
  from sagemaker.model import Model
  from sagemaker.serializers import CSVSerializer
  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 IPInsightsPredictor object from inherited base classes: Predictor with example: obj = IPInsightsPredictor(endpoint_name, sagemaker_session, serializer, deserializer, component_name)