class_docstr: Reshape layer (specification key: `reshape`).
  
  Args:
      shape (<i>int | iter[int]</i>): New shape
          (<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: ["Reshape","layer","(","specification","key",":","`","reshape","`",")",".","Args",":","shape","(","<","i",">","int","|","iter","[","int","]","<","\/i",">",")",":","New","shape","(","<","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 output_spec(self):
      output_spec = super().output_spec()
  
      if output_spec.size != util.product(xs=self.shape):
          raise TensorforceError.value(
              name="Reshape", argument="shape", value=self.shape
          )
      output_spec.shape = self.shape
  
      return output_spec
  
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 2 for calling the Reshape obj's underlying member method code with example usage: obj.output_spec() and returns: output_spec