class_docstr: Configuration for reading and parsing a tf.Tensor encoded with
  ATDS dense feature schema.
  
  Fields:
    shape: Shape of input data. Each dimension should be positive.
    dtype: Data type of input.
class_docstr_tok: ["Configuration","for","reading","and","parsing","a","tf.Tensor","encoded","with","ATDS","dense","feature","schema",".","Fields",":","shape",":","Shape","of","input","data",".","Each","dimension","should","be","positive",".","dtype",":","Data","type","of","input","."]
code: def __new__(cls, shape: List[int], dtype: tf.dtypes.DType):
      _validate_shape_and_dtype(shape, dtype)
      for dim in shape:
          if dim <= 0:
              raise ValueError(
                  f"Each dimension should be greater than 0"
                  f" in DenseFeature but found {shape}."
              )
  
      return super().__new__(cls, shape, dtype)
  
file_path: io/tensorflow_io/python/experimental/atds/features.py
imports: import collections
  from typing import List
  import tensorflow
label_desc: Use image node_id 1 for calling the DenseFeature obj's underlying member method code with example usage: obj.__new__(cls, shape, dtype) and returns: super