class_docstr: Handles Amazon SageMaker processing tasks for jobs using PyTorch containers.
class_docstr_tok: ["Handles","Amazon","SageMaker","processing","tasks","for","jobs","using","PyTorch","containers","."]
docstr: This processor executes a Python script in a PyTorch execution environment.
  
  Unless ``image_uri`` is specified, the PyTorch environment is an
  Amazon-built Docker container that executes functions defined in the supplied
  ``code`` Python script.
  
  The arguments have the exact same meaning as in ``FrameworkProcessor``.
  
  .. tip::
  
      You can find additional parameters for initializing this class at
      :class:`~sagemaker.processing.FrameworkProcessor`.
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code: def __init__(
      self,
      framework_version: str,  # New arg
      role: Optional[Union[str, PipelineVariable]] = None,
      instance_count: Union[int, PipelineVariable] = None,
      instance_type: Union[str, PipelineVariable] = None,
      py_version: str = "py3",  # New kwarg
      image_uri: Optional[Union[str, PipelineVariable]] = None,
      command: Optional[List[str]] = None,
      volume_size_in_gb: Union[int, PipelineVariable] = 30,
      volume_kms_key: Optional[Union[str, PipelineVariable]] = None,
      output_kms_key: Optional[Union[str, PipelineVariable]] = None,
      code_location: Optional[str] = None,  # New arg
      max_runtime_in_seconds: Optional[
          Union[int, PipelineVariable]
      ] = None,
      base_job_name: Optional[str] = None,
      sagemaker_session: Optional[Session] = None,
      env: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
      tags: Optional[Tags] = None,
      network_config: Optional[NetworkConfig] = None,
  ):
      """This processor executes a Python script in a PyTorch execution environment.
  
      Unless ``image_uri`` is specified, the PyTorch environment is an
      Amazon-built Docker container that executes functions defined in the supplied
      ``code`` Python script.
  
      The arguments have the exact same meaning as in ``FrameworkProcessor``.
  
      .. tip::
  
          You can find additional parameters for initializing this class at
          :class:`~sagemaker.processing.FrameworkProcessor`.
      """
      super().__init__(
          self.estimator_cls,
          framework_version,
          role,
          instance_count,
          instance_type,
          py_version,
          image_uri,
          command,
          volume_size_in_gb,
          volume_kms_key,
          output_kms_key,
          code_location,
          max_runtime_in_seconds,
          base_job_name,
          sagemaker_session,
          env,
          format_tags(tags),
          network_config,
      )
  
file_path: sagemaker-python-sdk/src/sagemaker/pytorch/processing.py
imports: from __future__ import absolute_import
  from typing import Union, Optional, List, Dict
  from sagemaker.session import Session
  from sagemaker.network import NetworkConfig
  from sagemaker.processing import FrameworkProcessor
  from sagemaker.pytorch.estimator import PyTorch
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.utils import format_tags, Tags
label_desc: Use image node_id 1 to create a new PyTorchProcessor object from inherited base classes: FrameworkProcessor with example: obj = PyTorchProcessor(framework_version, role, instance_count, instance_type, py_version, image_uri, command, volume_size_in_gb, volume_kms_key, output_kms_key, code_location, max_runtime_in_seconds, base_job_name, sagemaker_session, env, tags, network_config)