code: def convert_reshape(params, w_name, scope_name, inputs, layers, weights, names):
      
      print('Converting reshape ...')
      if names == 'short':
          tf_name = 'RESH' + random_string(4)
      elif names == 'keep':
          tf_name = w_name
      else:
          tf_name = w_name + str(random.random())
  
      if len(inputs) > 1:
          if layers[inputs[1]][0] == -1:
              print('Cannot deduct batch size! It will be omitted, but result may be wrong.')
  
          reshape = keras.layers.Reshape(layers[inputs[1] + '_np'], name=tf_name)
          layers[scope_name] = reshape(layers[inputs[0]])
      else:
          if inputs[0] in layers:
              reshape = keras.layers.Reshape(params['shape'][1:], name=tf_name)
              layers[scope_name] = reshape(layers[inputs[0]])
          else:
              print('Skip weight matrix transpose, but result may be wrong.')
docs: Convert reshape layer.
  
     Args:
          params: dictionary with layer parameters
          w_name: name prefix in state_dict
          scope_name: pytorch scope name
          inputs: pytorch node inputs
          layers: dictionary with keras tensors
          weights: pytorch state_dict
          names: use short names for keras layers