class_docstr: PReLU activation function.
  
  This is the elementwise function `x -> max(x, 0) + α * min(x, 0)`.
  This can be thought of as a leaky ReLU, with a learnt leak `α`.
docstr: **Arguments:**
  
  - `x`: The input.
  
  **Returns:**
  
  A JAX array of the same shape as the input.
docstr_tok: ["*","*","Arguments",":","*","*","-","`","x","`",":","The","input",".","*","*","Returns",":","*","*","A","JAX","array","of","the","same","shape","as","the","input","."]
code: def __call__(self, x: Array) -> Array:
      r"""**Arguments:**
  
      - `x`: The input.
  
      **Returns:**
  
      A JAX array of the same shape as the input.
      """
      with jax.numpy_dtype_promotion(
          "standard"
      ), jax.numpy_rank_promotion("allow"):
          return jnp.where(x >= 0, x, self.negative_slope * x)
  
file_path: equinox/equinox/nn/_activations.py
imports: from typing import Optional, Union
  import jax
  import jax.numpy
  from jaxtyping import Array
  from .._module import Module
label_desc: Use image node_id 2 for calling the PReLU obj's underlying member method code with example usage: obj.__call__(x) and returns: jnp