kind: ServiceAccount
        metadata:
          name: {{.ServiceAccount | quote}}
          namespace: {{.Namespace | quote}}
          annotations:
            {{- toJsonMap (omit .InfrastructureAnnotations "kubectl.kubernetes.io/last-applied-configuration" "gateway.istio.io/name-override" "gateway.istio.io/service-account" "gateway.istio.io/controller-version") | nindent 4 }}
          labels:

		{"(*Stmt).ExecContext", Method, 8},
		{"(*Stmt).Query", Method, 0},
		{"(*Stmt).QueryContext", Method, 8},
		{"(*Stmt).QueryRow", Method, 0},
		{"(*Stmt).QueryRowContext", Method, 8},
		{"(*Tx).Commit", Method, 0},
		{"(*Tx).Exec", Method, 0},
		{"(*Tx).ExecContext", Method, 8},
		{"(*Tx).Prepare", Method, 0},
		{"(*Tx).PrepareContext", Method, 8},
		{"(*Tx).Query", Method, 0},

  in the 'FROM' directive of the server image Dockerfiles.
  
  As a result, server images for non-amd64 images copy in the go-runner
  amd64 binary instead of the go-runner that matches that architecture.
  
  This commit explicitly sets the '--platform=linux/${arch}' to ensure
  we're pulling the correct go-runner arch from the manifest list.
  
  Before:
  `FROM ${base_image}`
  
  After:

            "default": "",
            "description": "repository is the URL",
            "type": "string"
          },
          "revision": {
            "description": "revision is the commit hash for the specified revision.",
            "type": "string"
          }
        },
        "required": [
          "repository"
        ],
        "type": "object"
      },

*   `tf.nn`

    *   `tf.nn.embedding_lookup_sparse` and `tf.nn.safe_embedding_lookup_sparse` now support ids and weights described by `tf.RaggedTensor`s.

special meaning, namely `,.->`. The behavior of this Op is undefined if it
receives an ill-formatted equation; since the validation is done at
graph-building time, we omit format validation checks at runtime.

Note: This Op is *not* intended to be called by the user; instead users should
call `tf.einsum` directly. It is a hidden Op used by `tf.einsum`.