class_docstr: Block that aggregates Convolution + Norm layer + ReLU.
  
  Attributes:
    kernel_size: Integer, convolution kernel size.
    out_dim: Integer, number of filter in the convolution.
    norm_type: Object type of the norm to use.
    stride: Integer, stride used in the convolution.
    input_shape: Tuple with the input shape i.e. (None, 512, 512, 3).
    padding: String, padding type used in the convolution.
    relu: Boolean, use relu at the end.
    name: String, name of the block.
class_docstr_tok: ["Block","that","aggregates","Convolution","+","Norm","layer","+","ReLU",".","Attributes",":","kernel_size",":","Integer",",","convolution","kernel","size",".","out_dim",":","Integer",",","number","of","filter","in","the","convolution",".","norm_type",":","Object","type","of","the","norm","to","use",".","stride",":","Integer",",","stride","used","in","the","convolution",".","input_shape",":","Tuple","with","the","input","shape","i.e",".","(","None",",","512",",","512",",","3",")",".","padding",":","String",",","padding","type","used","in","the","convolution",".","relu",":","Boolean",",","use","relu","at","the","end",".","name",":","String",",","name","of","the","block","."]
code: def call(self, inputs, **kwargs):
      x = self.pad(inputs)
      x = self.conv(x)
      x = self.norm(x)
      x = self.relu(x)
      return x
  
file_path: graphics/tensorflow_graphics/projects/points_to_3Dobjects/networks/custom_blocks.py
imports: import tensorflow
label_desc: Use image node_id 2 for calling the ConvolutionalBlock obj's underlying member method code with example usage: obj.call(inputs) and returns: x