node {
      name: "Const"
      op: "Const"
      attr {
        key: "_output_shapes"
        value {
          list {
            shape {
              dim {
                size: 150
              }
              dim {
                size: 150
              }
            }
          }
        }
      }
      attr {
        key: "dtype"
        value {

The conversion can happen partially in subgroups, by specifying the dim
attribute, where only that dim will be converted.
  }];

  let arguments = (ins
    TF_Tensor:$input,

    StrAttr:$manual_sharding,
    DefaultValuedOptionalAttr<I64Attr, "-1">:$dim,
    DefaultValuedOptionalAttr<I64ArrayAttr, "{}">:$unspecified_dims
  );

  let results = (outs

		{"(*UnsupportedTypeError).Error", Method, 0},
		{"(*UnsupportedValueError).Error", Method, 0},
		{"(Delim).String", Method, 5},
		{"(Number).Float64", Method, 1},
		{"(Number).Int64", Method, 1},
		{"(Number).String", Method, 1},
		{"(RawMessage).MarshalJSON", Method, 8},
		{"Compact", Func, 0},
		{"Decoder", Type, 0},
		{"Delim", Type, 5},
		{"Encoder", Type, 0},
		{"HTMLEscape", Func, 0},
		{"Indent", Func, 0},

03ED          ; valid                                  # 1.1  COPTIC SMALL LETTER SHIMA
03EE          ; mapped                 ; 03EF          # 1.1  COPTIC CAPITAL LETTER DEI
03EF          ; valid                                  # 1.1  COPTIC SMALL LETTER DEI
03F0          ; mapped                 ; 03BA          # 1.1  GREEK KAPPA SYMBOL
03F1          ; mapped                 ; 03C1          # 1.1  GREEK RHO SYMBOL

* `tf.argmax`: `dimension` becomes `axis` * `tf.argmin`: `dimension` becomes
`axis` * `tf.count_nonzero`: `reduction_indices` becomes `axis` *
`tf.expand_dims`: `dim` becomes `axis` * `tf.reduce_all`: `reduction_indices`
becomes `axis` * `tf.reduce_any`: `reduction_indices` becomes `axis` *
`tf.reduce_join`: `reduction_indices` becomes `axis` * `tf.reduce_logsumexp`: