code: def pdist(a: TensorLikeType, p: float = 2) -> TensorLikeType:
      torch._check(
          a.ndim == 2,
          lambda: f"pdist only supports 2D tensors, got: {a.ndim}D",
      )
      torch._check(
          p >= 0, lambda: "pdist only supports non-negative p values"
      )
      # For p == 2 we can use an efficient implementation, but other values of p
      # require creating a much bigger tensor for an intermediate step
      if p == 2:
          aTa = torch.mm(a, a.T)
          aTa_diag = torch.diag(aTa)
          t = torch.sqrt(
              torch.clamp(
                  aTa_diag + aTa_diag.unsqueeze(-1) - 2 * aTa, min=0
              )
          )
      else:
          t = torch.linalg.vector_norm(a.unsqueeze(1) - a, ord=p, dim=2)
      i = torch.triu_indices(
          t.shape[0], t.shape[1], offset=1, device=a.device
      )
      return t.flatten().index_select(0, i[0] * t.shape[0] + i[1])
  
imports: import math
  from functools import wraps
  from typing import Callable, Optional, Union
  import torch
  import torch._prims
  import torch._prims_common
  import torch._refs
  from torch._decomp import register_decomposition
  from torch._prims_common import ELEMENTWISE_TYPE_PROMOTION_KIND, NumberType, ShapeType, TensorLike, TensorLikeType
  from torch._prims_common.wrappers import elementwise_type_promotion_wrapper, elementwise_unary_scalar_wrapper, out_wrapper
  from torch._refs import _make_inplace
label_desc: Use image node_id 42 for calling a global function with example usage: pdist(a, p) and returns: t