class_docstr: Applies word dropout to a batch of input IDs.
  
  This is basically the same as `nn.Dropout`, but allows specifying the
  value of dropped out items.
code: def __call__(
      self, inputs: Array, deterministic: Optional[bool] = None
  ):
      deterministic = nn.module.merge_param(
          "deterministic", self.deterministic, deterministic
      )
      if deterministic or self.dropout_rate == 0.0:
          return inputs
      rng = self.make_rng("dropout")
      mask = jax.random.bernoulli(
          rng, p=self.dropout_rate, shape=inputs.shape
      )
      return jnp.where(mask, jnp.array([self.unk_idx]), inputs)
  
imports: import functools
  from typing import Any, Callable, Optional
  from flax import linen
  import jax
  from jax import numpy
label_desc: Use image node_id 1 for calling the WordDropout obj's underlying member method code with example usage: obj.__call__(inputs, deterministic) and returns: jnp, inputs