class_docstr: Upsample the spatial dimensions (last two dimensions) of the input
  by integer scale factors using nearest-neighbor interpolation.
  
  Parameters
  ----------
  x: tensor<[\*D, H1, W1],T>  (Required)
      * Must be at least rank ``3``.
  scale_factor_height: const<i32> or const<fp32> (Optional, default=1)
      * Scale factor for the height dimension (``axis=-2``).
      * Can be either an integer or fractional.
  scale_factor_width: const<i32> or const<fp32> (Optional, default=1)
      * Scale factor for the width dimension (``axis=-1``).
      * Can be either an integer or fractional.
  
  Returns
  -------
  tensor<[\*D, H2, W2],T>
      * Tensor with same type as the input.
      * ``H2`` = floor(``H1`` * ``scale_factor_height``).
      * ``W2`` = floor(``W1`` * ``scale_factor_width``).
  
  Attributes
  ----------
  T: fp16, fp32
  U: fp32, i32
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code: def default_inputs(self):
      return DefaultInputs(
          scale_factor_height=1,
          scale_factor_width=1,
      )
  
imports: import numpy
  from coremltools.converters.mil.mil import DefaultInputs, InputSpec, Operation, TensorInputType, get_new_symbol, types
  from coremltools.converters.mil.mil.ops.defs._op_reqs import register_op
  from coremltools.converters.mil.mil.ops.defs.iOS15 import _IOS15_TARGET
  from coremltools.converters.mil.mil.types.symbolic import is_symbolic
label_desc: Use image node_id 1 for calling the upsample_nearest_neighbor obj's underlying member method code with example usage: obj.default_inputs() and returns: DefaultInputs