class_docstr: ResNet50 module.
class_docstr_tok: ["ResNet50","module","."]
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 `BatchNorm` layers.
    resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults to
      False.
    name: Name of the module.
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code: def __init__(
      self,
      num_classes: int,
      bn_config: Optional[Mapping[str, float]] = None,
      resnet_v2: bool = False,
      name: Optional[str] = None,
  ):
      """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 `BatchNorm` layers.
        resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults to
          False.
        name: Name of the module.
      """
      super().__init__(
          [3, 4, 6, 3],
          num_classes=num_classes,
          bn_config=bn_config,
          resnet_v2=resnet_v2,
          name=name,
      )
  
imports: from typing import Mapping, Optional, Sequence, Union
  from sonnet.src import base
  from sonnet.src import batch_norm
  from sonnet.src import conv
  from sonnet.src import initializers
  from sonnet.src import linear
  from sonnet.src import pad
  import tensorflow
label_desc: Use image node_id 1 to create a new ResNet50 object from inherited base classes: ResNet with example: obj = ResNet50(num_classes, bn_config, resnet_v2, name)