parameters: (self, output)
return_statement: return np.reshape(output, (dim1, dim2, dim3, dim4))
docstring: Reshape a TensorRT output from NCHW to NHWC format (with expected C=255),
          and then return it in (height,width,3,85) dimensionality after further reshaping.
  
          Keyword argument:
          output -- an output from a TensorRT engine after inference
docstring_summary: Reshape a TensorRT output from NCHW to NHWC format (with expected C=255),
          and then return it in (height,width,3,85) dimensionality after further reshaping.
docstring_tokens: Reshape a TensorRT output from NCHW to NHWC format ( with expected C = 255 ) and then return it in ( height width 3 85 ) dimensionality after further reshaping .
function: def _reshape_output(self, output):
          """Reshape a TensorRT output from NCHW to NHWC format (with expected C=255),
          and then return it in (height,width,3,85) dimensionality after further reshaping.
  
          Keyword argument:
          output -- an output from a TensorRT engine after inference
          """
          output = np.transpose(output, [0, 2, 3, 1])
          _, height, width, _ = output.shape
          dim1, dim2 = height, width
          dim3 = 3
          # There are CATEGORY_NUM=80 object categories:
          dim4 = (4 + 1 + CATEGORY_NUM)
          return np.reshape(output, (dim1, dim2, dim3, dim4))
function_tokens: def _reshape_output ( self , output ) : output = np . transpose ( output , [ 0 , 2 , 3 , 1 ] ) _ , height , width , _ = output . shape dim1 , dim2 = height , width dim3 = 3 # There are CATEGORY_NUM=80 object categories: dim4 = ( 4 + 1 + CATEGORY_NUM ) return np . reshape ( output , ( dim1 , dim2 , dim3 , dim4 ) )