class_docstr: Local linear layer, i.e. Conv1dLocal() with filter size 1.
docstr: Create local linear layers.
  Transformations of the feature are independent of each other,
  each feature is expanded to several hidden units.
  
  Args:
      num_linear: num of local linear layers.
      input_features: m1.
      output_features: m2.
      bias: whether to include bias or not.
docstr_tok: ["Create","local","linear","layers",".","Transformations","of","the","feature","are","independent","of","each","other",",","each","feature","is","expanded","to","several","hidden","units",".","Args",":","num_linear",":","num","of","local","linear","layers",".","input_features",":","m1",".","output_features",":","m2",".","bias",":","whether","to","include","bias","or","not","."]
code: def __init__(
      self,
      num_linear: int,
      input_features: int,
      output_features: int,
      bias: bool = True,
  ):
      """
      Create local linear layers.
      Transformations of the feature are independent of each other,
      each feature is expanded to several hidden units.
  
      Args:
          num_linear: num of local linear layers.
          input_features: m1.
          output_features: m2.
          bias: whether to include bias or not.
      """
      super().__init__()
      self.num_linear = num_linear
      self.input_features = input_features
      self.output_features = output_features
  
      self.weight = nn.Parameter(
          torch.Tensor(num_linear, input_features, output_features)
      )
      if bias:
          self.bias = nn.Parameter(
              torch.Tensor(num_linear, output_features)
          )
      else:
          # You should always register all possible parameters, but the
          # optional ones can be None if you want.
          self.register_parameter("bias", None)
  
      self.reset_parameters()
  
file_path: causalnex/causalnex/structure/pytorch/nonlinear.py
imports: import math
  import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new LocallyConnected object from inherited base classes: nn with example: obj = LocallyConnected(num_linear, input_features, output_features, bias)