class_docstr: CoreML internal representation of a torch IR block.
  
  Arguments:
      raw_block: The torch._C.Block to convert, or None.
      parent: The InternalTorchIRNode this block belongs to.
      nodes: If @raw_block is None, the list of InternalTorchIRNodes in the block
      inputs: If @raw_block is None, the list of input symbols.
      outputs: If @raw_block is None, the list of output symbols.
code: def __init__(
      self,
      raw_block=None,
      parent=None,
      nodes=None,
      inputs=None,
      outputs=None,
  ):
      self.nodes = []
      node_names = set()
      self.inputs = []
      self.outputs = []
      self.parent = parent
  
      if raw_block:
          # Add nodes
          for raw_node in raw_block.nodes():
              new_node = InternalTorchIRNode(raw_node, parent=self)
              if new_node.name == new_node.kind:
                  new_node.name = _find_new_name(
                      new_node.name, node_names
                  )
              self.nodes.append(new_node)
              node_names.add(new_node.name)
  
          # Add inputs
          for inp in raw_block.inputs():
              self.inputs.append(inp.debugName())
  
          # Add outputs
          for outp in raw_block.outputs():
              self.outputs.append(outp.debugName())
      else:
          self.nodes = nodes
          self.inputs = inputs
          self.outputs = outputs
  
imports: from collections import OrderedDict
  from itertools import islice
  import torch
label_desc: Use image node_id 1 to create a new InternalTorchIRBlock object  with example: obj = InternalTorchIRBlock(raw_block, parent, nodes, inputs, outputs)