class_docstr: A sample path in the form of a generalized linear model.
docstr: Initializes a GeneralizedLinearPath instance.
  
  .. code-block:: text
  
      path(x) = output_transform(bias_module(z) + feature_map(z)^T weight),
      where z = input_transform(x).
  
  Args:
      feature_map: A map used to featurize the module's inputs.
      weight: A tensor of weights used to combine input features.
      bias_module: An optional module used to define additive offsets.
      input_transform: An optional input transform for the module.
      output_transform: An optional output transform for the module.
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code: def __init__(
      self,
      feature_map: FeatureMap,
      weight: Union[Parameter, Tensor],
      bias_module: Optional[Module] = None,
      input_transform: Optional[TInputTransform] = None,
      output_transform: Optional[TOutputTransform] = None,
  ):
      r"""Initializes a GeneralizedLinearPath instance.
  
      .. code-block:: text
  
          path(x) = output_transform(bias_module(z) + feature_map(z)^T weight),
          where z = input_transform(x).
  
      Args:
          feature_map: A map used to featurize the module's inputs.
          weight: A tensor of weights used to combine input features.
          bias_module: An optional module used to define additive offsets.
          input_transform: An optional input transform for the module.
          output_transform: An optional output transform for the module.
      """
      super().__init__()
      self.feature_map = feature_map
      if not isinstance(weight, Parameter):
          self.register_buffer("weight", weight)
      self.weight = weight
      self.bias_module = bias_module
      self.input_transform = input_transform
      self.output_transform = output_transform
  
imports: from __future__ import annotations
  from abc import ABC
  from typing import Any, Callable, Dict, Iterable, Iterator, List, Mapping, Optional, Tuple, Union
  from botorch.exceptions.errors import UnsupportedError
  from botorch.sampling.pathwise.features import FeatureMap
  from botorch.sampling.pathwise.utils import TInputTransform, TOutputTransform, TransformedModuleMixin
  from torch import Tensor
  from torch.nn import Module, ModuleDict, ModuleList, Parameter
label_desc: Use image node_id 1 to create a new GeneralizedLinearPath object from inherited base classes: SamplePath with example: obj = GeneralizedLinearPath(feature_map, weight, bias_module, input_transform, output_transform)