class_docstr: Dropout layer (specification key: `dropout`).
  
  Args:
      rate (parameter, 0.0 <= float < 1.0): Dropout rate
          (<span style="color:#C00000"><b>required</b></span>).
      name (string): Layer name
          (<span style="color:#00C000"><b>default</b></span>: internally chosen).
      input_spec (specification): <span style="color:#00C000"><b>internal use</b></span>.
class_docstr_tok: ["Dropout","layer","(","specification","key",":","`","dropout","`",")",".","Args",":","rate","(","parameter",",","0.0","<","=","float","<","1.0",")",":","Dropout","rate","(","<","span","style=","''","color",":","#","C00000","''",">","<","b",">","required","<","\/b",">","<","\/span",">",")",".","name","(","string",")",":","Layer","name","(","<","span","style=","''","color",":","#","00C000","''",">","<","b",">","default","<","\/b",">","<","\/span",">",":","internally","chosen",")",".","input_spec","(","specification",")",":","<","span","style=","''","color",":","#","00C000","''",">","<","b",">","internal","use","<","\/b",">","<","\/span",">","."]
code: def __init__(self, *, rate, name=None, input_spec=None):
      super().__init__(name=name, input_spec=input_spec)
  
      # Rate
      self.rate = self.submodule(
          name="rate",
          module=rate,
          modules=parameter_modules,
          dtype="float",
          min_value=0.0,
          max_value=1.0,
      )
  
      self.architecture_kwargs["rate"] = str(rate)
  
imports: import math
  import random
  import numpy
  import tensorflow
  from tensorforce import TensorforceError, util
  from tensorforce.core import parameter_modules, TensorSpec, tf_function, tf_util
  from tensorforce.core.layers import Layer, NondeterministicLayer
label_desc: Use image node_id 1 to create a new Dropout object from inherited base classes: NondeterministicLayer with example: obj = Dropout()