class_docstr: ResNet101.
class_docstr_tok: ["ResNet101","."]
docstr: Constructs a ResNet model.
  
  Args:
    num_classes: The number of classes to classify the inputs into.
    bn_config: A dictionary of two elements, ``decay_rate`` and ``eps`` to be
      passed on to the :class:`~haiku.BatchNorm` layers.
    resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults
      to ``False``.
    logits_config: A dictionary of keyword arguments for the logits layer.
    name: Name of the module.
    initial_conv_config: Keyword arguments passed to the constructor of the
      initial :class:`~haiku.Conv2D` module.
    strides: A sequence of length 4 that indicates the size of stride
      of convolutions for each block in each group.
docstr_tok: ["Constructs","a","ResNet","model",".","Args",":","num_classes",":","The","number","of","classes","to","classify","the","inputs","into",".","bn_config",":","A","dictionary","of","two","elements",",","``","decay_rate","``","and","``","eps","``","to","be","passed","on","to","the",":","class",":","`","~haiku.BatchNorm","`","layers",".","resnet_v2",":","Whether","to","use","the","v1","or","v2","ResNet","implementation",".","Defaults","to","``","False","``",".","logits_config",":","A","dictionary","of","keyword","arguments","for","the","logits","layer",".","name",":","Name","of","the","module",".","initial_conv_config",":","Keyword","arguments","passed","to","the","constructor","of","the","initial",":","class",":","`","~haiku.Conv2D","`","module",".","strides",":","A","sequence","of","length","4","that","indicates","the","size","of","stride","of","convolutions","for","each","block","in","each","group","."]
code: def __init__(
      self,
      num_classes: int,
      bn_config: Optional[Mapping[str, FloatStrOrBool]] = None,
      resnet_v2: bool = False,
      logits_config: Optional[Mapping[str, Any]] = None,
      name: Optional[str] = None,
      initial_conv_config: Optional[
          Mapping[str, FloatStrOrBool]
      ] = None,
      strides: Sequence[int] = (1, 2, 2, 2),
  ):
      """Constructs a ResNet model.
  
      Args:
        num_classes: The number of classes to classify the inputs into.
        bn_config: A dictionary of two elements, ``decay_rate`` and ``eps`` to be
          passed on to the :class:`~haiku.BatchNorm` layers.
        resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults
          to ``False``.
        logits_config: A dictionary of keyword arguments for the logits layer.
        name: Name of the module.
        initial_conv_config: Keyword arguments passed to the constructor of the
          initial :class:`~haiku.Conv2D` module.
        strides: A sequence of length 4 that indicates the size of stride
          of convolutions for each block in each group.
      """
      super().__init__(
          num_classes=num_classes,
          bn_config=bn_config,
          initial_conv_config=initial_conv_config,
          resnet_v2=resnet_v2,
          strides=strides,
          logits_config=logits_config,
          name=name,
          **ResNet.CONFIGS[101],
      )
  
imports: from collections.abc import Mapping, Sequence
  from typing import Any, Optional, Union
  from haiku._src import basic
  from haiku._src import batch_norm
  from haiku._src import conv
  from haiku._src import module
  from haiku._src import pool
  import jax
  import jax.numpy
label_desc: Use image node_id 1 to create a new ResNet101 object from inherited base classes: ResNet with example: obj = ResNet101(num_classes, bn_config, resnet_v2, logits_config, name, initial_conv_config, strides)