class_docstr: Applies dropout.
  
  Note that this layer behaves differently during training and inference. During
  training then dropout is randomly applied; during inference this layer does nothing.
  Whether the model is in training or inference mode should be toggled using
  [`equinox.nn.inference_mode`][].
class_docstr_tok: ["Applies","dropout",".","Note","that","this","layer","behaves","differently","during","training","and","inference",".","During","training","then","dropout","is","randomly","applied",";","during","inference","this","layer","does","nothing",".","Whether","the","model","is","in","training","or","inference","mode","should","be","toggled","using","[","`","equinox.nn.inference_mode","`","]","[","]","."]
code: def deterministic(self):
      return self.inference
  
file_path: equinox/equinox/nn/_dropout.py
imports: import warnings
  from typing import Optional
  import jax
  import jax.lax
  import jax.numpy
  import jax.random
  from jaxtyping import Array, PRNGKeyArray
  from .._module import Module
label_desc: Use image node_id 2 for calling the Dropout obj's underlying member method code with example usage: obj.deterministic() and returns: self