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# torch.jit.load

torch.jit.load(f, map_location=None, _extra_files=None, _restore_shapes=False)

  Load a `ScriptModule` or `ScriptFunction` previously saved with `torch.jit.save`

  All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from. If this fails (e.g. because the run time system doesn’t have certain devices), an exception is raised.

  Parameters:

    - f – a file-like object (has to implement read, readline, tell, and seek), or a string containing a file name

    - map_location (string or torch.device) – A simplified version of `map_location` in torch.jit.save used to dynamically remap storages to an alternative set of devices.

    - _extra_files (dictionary of filename to content) – The extra filenames given in the map would be loaded and their content would be stored in the provided map.

    - _restore_shapes (bool) – Whether or not to retrace the module on load using stored inputs

  Returns:

    A `ScriptModule` object.

  Example:

      import torch
      import io

      torch.jit.load('scriptmodule.pt')

      # Load ScriptModule from io.BytesIO object
      with open('scriptmodule.pt', 'rb') as f:
          buffer = io.BytesIO(f.read())

      # Load all tensors to the original device
      torch.jit.load(buffer)

      # Load all tensors onto CPU, using a device
      buffer.seek(0)
      torch.jit.load(buffer, map_location=torch.device('cpu'))

      # Load all tensors onto CPU, using a string
      buffer.seek(0)
      torch.jit.load(buffer, map_location='cpu')

      # Load with extra files.
      extra_files = {'foo.txt': ''}  # values will be replaced with data
      torch.jit.load('scriptmodule.pt', _extra_files=extra_files)
      print(extra_files['foo.txt'])

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