class_docstr: Linear module, optionally including bias.
docstr: Constructs a `Linear` module.
  
  Args:
    output_size: Output dimensionality.
    with_bias: Whether to include bias parameters. Default `True`.
    w_init: Optional initializer for the weights. By default the weights are
      initialized truncated random normal values with a standard deviation of
      `1 / sqrt(input_feature_size)`, which is commonly used when the inputs
      are zero centered (see https://arxiv.org/abs/1502.03167v3).
    b_init: Optional initializer for the bias. By default the bias is
      initialized to zero.
    name: Name of the module.
code: def __init__(
      self,
      output_size: int,
      with_bias: bool = True,
      w_init: Optional[initializers.Initializer] = None,
      b_init: Optional[initializers.Initializer] = None,
      name: Optional[str] = None,
  ):
      """Constructs a `Linear` module.
  
      Args:
        output_size: Output dimensionality.
        with_bias: Whether to include bias parameters. Default `True`.
        w_init: Optional initializer for the weights. By default the weights are
          initialized truncated random normal values with a standard deviation of
          `1 / sqrt(input_feature_size)`, which is commonly used when the inputs
          are zero centered (see https://arxiv.org/abs/1502.03167v3).
        b_init: Optional initializer for the bias. By default the bias is
          initialized to zero.
        name: Name of the module.
      """
      super().__init__(name=name)
      self.output_size = output_size
      self.with_bias = with_bias
      self.w_init = w_init
      if with_bias:
          self.b_init = (
              b_init if b_init is not None else initializers.Zeros()
          )
      elif b_init is not None:
          raise ValueError(
              "When not using a bias the b_init must be None."
          )
  
file_path: sonnet/sonnet/src/linear.py
imports: import math
  from typing import Optional
  from sonnet.src import base
  from sonnet.src import initializers
  from sonnet.src import once
  from sonnet.src import utils
  import tensorflow
label_desc: Use image node_id 1 to create a new Linear object from inherited base classes: base with example: obj = Linear(output_size, with_bias, w_init, b_init, name)