class_docstr: Linear module.
docstr: Constructs the Linear module.
  
  Args:
    output_size: Output dimensionality.
    with_bias: Whether to add a bias to the output.
    w_init: Optional initializer for weights. By default, uses random values
      from truncated normal, with stddev ``1 / sqrt(fan_in)``. See
      https://arxiv.org/abs/1502.03167v3.
    b_init: Optional initializer for bias. By default, zero.
    name: Name of the module.
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code: def __init__(
      self,
      output_size: int,
      with_bias: bool = True,
      w_init: Optional[hk.initializers.Initializer] = None,
      b_init: Optional[hk.initializers.Initializer] = None,
      name: Optional[str] = None,
  ):
      """Constructs the Linear module.
  
      Args:
        output_size: Output dimensionality.
        with_bias: Whether to add a bias to the output.
        w_init: Optional initializer for weights. By default, uses random values
          from truncated normal, with stddev ``1 / sqrt(fan_in)``. See
          https://arxiv.org/abs/1502.03167v3.
        b_init: Optional initializer for bias. By default, zero.
        name: Name of the module.
      """
      super().__init__(name=name)
      self.input_size = None
      self.output_size = output_size
      self.with_bias = with_bias
      self.w_init = w_init
      self.b_init = b_init or jnp.zeros
  
imports: from collections.abc import Iterable, Sequence
  import functools
  from typing import Any, Callable, Optional
  from haiku._src import base
  from haiku._src import initializers
  from haiku._src import module
  from haiku._src import typing
  from haiku._src.typing import PRNGKey
  import jax
  from jax import lax
  import jax.numpy
  import numpy
label_desc: Use image node_id 1 to create a new Linear object from inherited base classes: hk with example: obj = Linear(output_size, with_bias, w_init, b_init, name)