class_docstr: Inspiration Layer (from MSG-Net paper)
  tuning the featuremap with target Gram Matrix
  ref https://arxiv.org/abs/1703.06953
  
  Args:
     C(int): Number of input channels.
     B(int):  B is equal to 1 or input mini_batch, default is 1.
  
  Return:
      img(paddle.Tensor): UpBottleneck output.
class_docstr_tok: ["Inspiration","Layer","(","from","MSG-Net","paper",")","tuning","the","featuremap","with","target","Gram","Matrix","ref","https",":","\/\/arxiv.org\/abs\/1703.06953","Args",":","C","(","int",")",":","Number","of","input","channels",".","B","(","int",")",":","B","is","equal","to","1","or","input","mini_batch",",","default","is","1",".","Return",":","img","(","paddle.Tensor",")",":","UpBottleneck","output","."]
code: def __init__(self, C: int, B: int = 1):
      super(Inspiration, self).__init__()
  
      self.weight = self.weight = paddle.create_parameter(
          shape=[1, C, C], dtype="float32"
      )
      # non-parameter buffer
      self.G = paddle.to_tensor(np.random.rand(B, C, C))
      self.C = C
  
imports: import os
  import argparse
  import paddle
  import paddle.nn
  import numpy
  import paddle.nn.functional
  from paddlehub.env import MODULE_HOME
  from paddlehub.module.module import moduleinfo
  from paddlehub.vision.transforms import Compose, Resize, CenterCrop
  from paddlehub.module.cv_module import StyleTransferModule
label_desc: Use image node_id 1 to create a new Inspiration object from inherited base classes: nn with example: obj = Inspiration(C, B)