Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets

kronos-venv/lib/python3.12/site-packages/torch/__future__.py

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+_overwrite_module_params_on_conversion: bool = False
+_swap_module_params_on_conversion: bool = False
+
+
+def set_overwrite_module_params_on_conversion(value: bool) -> None:
+    """
+    Sets whether to assign new tensors to the parameters instead of changing the
+    existing parameters in-place when converting an ``nn.Module``.
+
+    When enabled, the following methods will assign new parameters to the module:
+
+    #. ``module.{device}()`` (e.g. :meth:`nn.Module.cuda()`) for moving a module between devices
+    #. ``module.{dtype}()`` (e.g. :meth:`nn.Module.float()`) for converting a module to a different dtype
+    #. :meth:`nn.Module.to`
+    #. :meth:`nn.Module.to_empty`
+
+    Args:
+        value (bool): Whether to assign new tensors or not.
+
+    """
+    global _overwrite_module_params_on_conversion
+    _overwrite_module_params_on_conversion = value
+
+
+def get_overwrite_module_params_on_conversion() -> bool:
+    """
+    Returns whether to assign new tensors to the parameters instead of changing the
+    existing parameters in-place when converting an :class:`torch.nn.Module`. Defaults to ``False``.
+
+    See :func:`~torch.__future__.set_overwrite_module_params_on_conversion` for more information.
+    """
+    return _overwrite_module_params_on_conversion
+
+
+def set_swap_module_params_on_conversion(value: bool) -> None:
+    """
+    Sets whether to use :func:`~torch.utils.swap_tensors` instead of setting ``.data`` to
+    change the existing parameters in-place when converting an ``nn.Module`` and instead
+    of ``param.copy_(state_dict[key])`` when loading a state dict into an ``nn.Module``.
+
+    .. note::
+        This function takes precedence over :func:`~torch.__future__.get_overwrite_module_params_on_conversion`
+
+    When enabled, the following methods will swap the existing parameters in-place:
+
+    #. ``module.{device}()`` (e.g. :meth:`nn.Module.cuda()`) for moving a module between devices
+    #. ``module.{dtype}()`` (e.g. :meth:`nn.Module.float()`) for converting a module to a different dtype
+    #. :meth:`nn.Module.to`
+    #. :meth:`nn.Module.to_empty`
+    #. :meth:`nn.Module.load_state_dict`
+
+    The semantics for :meth:`~nn.Module.load_state_dict` when this is set are as follows:
+
+    #. For each parameter/buffer, its corresponding ``state_dict['key']`` is transformed via
+       :meth:`~torch.Tensor.module_load` (i.e. ``res = param.module_load(state_dict['key'])``)
+    #. If necessary, ``res`` will be wrapped in an :class:`~nn.Parameter`
+    #. The parameter/buffer in the module will be swapped via :func:`~torch.utils.swap_tensors`
+       with ``res``
+
+    Args:
+        value (bool): Whether to use :func:`~torch.utils.swap_tensors` or not.
+
+    """
+    global _swap_module_params_on_conversion
+    _swap_module_params_on_conversion = value
+
+
+def get_swap_module_params_on_conversion() -> bool:
+    """
+    Returns whether to use :func:`~torch.utils.swap_tensors` instead of setting .data to
+    change the existing parameters in-place when converting an ``nn.Module``. Defaults to ``False``.
+
+    See :func:`~torch.__future__.set_swap_module_params_on_conversion` for more information.
+    """
+    return _swap_module_params_on_conversion