class_docstr: Remove single-dimension dimensions in a 1D or higher tensor.
  
  Parameters
  ----------
  x: tensor<*?,T> (Required)
      * Must be at least 1D.
  axes: const<K,i32> (Optional)
      * Axes to squeeze out.
      * default to remove all single-dimensions.
  
  Returns
  -------
  tensor<*(rank(x)-K),T>
      * Tensor with same type as input ``x`` and rank ``rank(x)-K``.
  
  Attributes
  ----------
  T: fp32
code: def value_inference(self):
      if self.x.val is None:
          return None
      if self.axes is None:
          return np.squeeze(self.x.val)
      else:
          return np.squeeze(self.x.val, axis=tuple(self.axes.val))
  
imports: import functools
  import sympy
  from coremltools.converters.mil.mil.types.symbolic import is_symbolic, isscalar, any_symbolic, any_variadic
  from coremltools.converters.mil.mil import get_new_symbol, get_new_variadic_symbol, VALUE, SYMBOL, types
  from ._op_reqs import *
label_desc: Use image node_id 3 for calling the squeeze obj's underlying member method code with example usage: obj.value_inference() and returns: None, np, np