class_docstr: Upsample the spatial dimensions (last two dimensions) of the input by
  scale factors using bilinear interpolation.
  It corresponds to `torch.nn.functional.interpolate` function with `mode=bilinear`,
  `recompute_scale_factor=True`, and input with flexible shape.
  source: https://pytorch.org/docs/stable/_modules/torch/nn/functional.html#interpolate
  
  Parameters
  ----------
  x: tensor<[b, C, H1, W1], T>  (Required)
      * Must be rank ``4``.
  output_height: i32
      * Output height for the height dimension.
  output_width: i32
      * Output width for the width dimension.
  aligh_corners: const<bool>
      * The `aligh_corners` parameter for the original torch op.
  
  Returns
  -------
  tensor<[b, C, H2, W2], T>
      * Tensor with same type as the input.
      * ``H2`` = output_height
      * ``W2`` = output_width
  
  Attributes
  ----------
  T: fp16, fp32
code: def default_inputs(self):
      return DefaultInputs(
          align_corners=True,
      )
  
file_path: coremltools/coremltools/converters/mil/frontend/torch/dialect_ops.py
imports: from coremltools.converters.mil.mil import Operation, get_new_symbol, types
  from coremltools.converters.mil.mil.input_type import DefaultInputs, InputSpec, TensorInputType
  from coremltools.converters.mil.mil.ops.defs._utils import get_param_val, solve_slice_by_index_shape
  from coremltools.converters.mil.mil.ops.registry import SSAOpRegistry
  from coremltools.converters.mil.mil.types.symbolic import is_compatible_symbolic_vector
label_desc: Use image node_id 1 for calling the torch_upsample_bilinear obj's underlying member method code with example usage: obj.default_inputs() and returns: DefaultInputs